July 16th - August 30th
10:00 - 19:00
10:00 - 19:00
Deadline
LOCATION:
New Delhi
New Delhi
REGISTRATIONS OPEN
00
Days
:
00
Hours
:
00
Minutes
REGISTRATION OPEN
Apply by August 30
Territorial Army Cyber Challenge:
Innovating for the Future of Defense
75 Years of Territorial Army
Building on the tremendous success of Terrier Cyber Quest 1.0, the Terrier Cyber Quest2.0 aims to delve deeper into fostering innovation and collaboration in critical domains like securing emerging technologies like drone technology, AI/ML, Quantum etc. With an expanded scope and challenging tracks, this phase continues the journey to unite India's brightest minds from academia, industry, and government to address modern defense challenges through technology.
July 16th - August 30th
10:00 - 19:00
10:00 - 19:00
Deadline
EVENT LOCATION
LOCATION: Delhi
REGISTRATIONS OPEN
00
Days
:
00
Hours
:
00
Minutes
Terrier Cyber Quest 2025 will have three tracks:
Track 1
AeroQuest
A thrilling drone race where participants test their aerial surveillance skills and tactical maneuvering. Pilots will navigate challenging courses, demonstrating precision and strategy to outpace their competitors.
Registration
Begins on July 16, 2025. Participants can register individually or in teams of up to three members. During registration, Applicants to share a video of racing through a track on Uncrashed FPV simulator (available on steam simulator).
Shortlisting
Top 10 Teams/Participants will be shortlisted based on their performance in the Uncrashed FPV simulator
Grand Finale
For the drone race event, visual inspectors equipped with flags will be stationed at each checkpoint to ensure no checkpoints are missed by the pilots. Safety nets will be provided for the checkpoint inspectors to ensure their protection. Shortlisted pilots will compete ingroups of three, with the number of groups determined based on the total number of pilots. The winning pilot from each group will advance to the next round, and pilots in subsequent rounds will be grouped according to their lap times
i. Finalists will demonstrate their fully functional nano drone prototypes in a controlled simulation environment.
ii. The drones will be assessed based on their maneuverability, camera resolution, stability, stealth capabilities, flight time, and ability to operate in scenarios mimicking real-world military conditions
i. Finalists will demonstrate their fully functional nano drone prototypes in a controlled simulation environment.
ii. The drones will be assessed based on their maneuverability, camera resolution, stability, stealth capabilities, flight time, and ability to operate in scenarios mimicking real-world military conditions
Key Takeaways
Aeroquest is a high-intensity drone racing challenge that
hones tactical maneuvering and aerial surveillance skills through real-world simulated scenarios. It fosters innovation in UAV technologies, enhances mission-critical decision-making, and encourages collaboration among defense personnel, technologists, and enthusiasts—ultimately contributing to national security and capacity building in drone-based
threat response.
hones tactical maneuvering and aerial surveillance skills through real-world simulated scenarios. It fosters innovation in UAV technologies, enhances mission-critical decision-making, and encourages collaboration among defense personnel, technologists, and enthusiasts—ultimately contributing to national security and capacity building in drone-based
threat response.
Timeline
Announcement
July 16, 2025
Registrations
July 16 - August 30, 2025
Shortlisting of participants
September 1 - 12, 2025
All entries received will be shortlisted in the first round by a screening committee identified by NCRB & Cyber Peace Foundation.
The shortlisted entries from the first round, will be further evaluated in the final round by the Jury composed of experts from the field. The participants will be required to give a presentation (10 minutes) followed by Q&A (5mins). During this time, they will also be given the opportunity to share applications, investigative aids, videos, tools and proof of concept. The Jury will select best 3 entries as winners of Track 2 .
Grand Finale
September 17, 2025
Award Ceremony
October 7, 2025
Regional Rounds
Pune, Kolkata, Udhampur, Lucknow and Chandigarh
August, 2025
August, 2025
Track 2
Capture The Flag
A race against the clock in high-stakes, Jeopardy-style or Network Simulation based CTF challenges, modeled after real-world cybersecurity scenarios. Participants must solve complex security puzzles, demonstrating their skills in hacking, defense, and problem-solving under pressure
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Competition Format:
Registration:
Registration for Terrier Cyber Quest 2025 begins on July 16,2025. Participants can register individually or in teams of up to three members. To strengthen their application, participants are encouraged to share links to certifications or badges earned in the cybersecurity domain.
Shortlisting
A 8-10 hour virtual elimination round will be conducted in this track. Teams/Participants will be provided with a link to the CTF Portal, where they will be competing against each other
● It will be a short Capture the Flag (CTF) event.
● Participants will be provided with puzzles/programs/system/files containing security vulnerabilities
● Each puzzle has a secret key called a 'flag' embedded within it. Finding the flag proves that participants have solved the particular challenge, and submitting the flag earns points.
● Each problem statement has its own points, which depend on the difficulty of the problem.
● The scoring procedure depends on how many points participants have earned and how much time they have taken to submit the flags.
● Participants also may be asked to submit a Proof-of-Concept document after this phase
● Top 10 teams/participants (maximum 10x3=30 headcounts) on the leadership board will be selected as thefinalists for this track
● Participants will be provided with puzzles/programs/system/files containing security vulnerabilities
● Each puzzle has a secret key called a 'flag' embedded within it. Finding the flag proves that participants have solved the particular challenge, and submitting the flag earns points.
● Each problem statement has its own points, which depend on the difficulty of the problem.
● The scoring procedure depends on how many points participants have earned and how much time they have taken to submit the flags.
● Participants also may be asked to submit a Proof-of-Concept document after this phase
● Top 10 teams/participants (maximum 10x3=30 headcounts) on the leadership board will be selected as thefinalists for this track
Grand Finale:
At the final stage, top 10 (maximum 10x3=30 head counts)shortlisted teams will engage in a sophisticated, real-time attack simulation. Participants will be challenged to compromise simulated railway and power sector OT/ICS environments while simultaneously attempting to exploit adversary systems. The event will reflect real-world cyber warfare conditions and test participants’ endurance, teamwork, and cyber defense strategy
● The Grand Finale will be an in-person event.
● During this phase, participants will tackle advanced challenges. The event will test their technical expertise, problem-solving abilities, and endurance, as they work to solve complex problems.
● This final round will determine the ultimate winners, showcasing the best talent in a highly competitive environment.
● Top 3 teams/individuals (maximum 3x3=9 head counts) will be declared as winners
● The Grand Finale will be an in-person event.
● During this phase, participants will tackle advanced challenges. The event will test their technical expertise, problem-solving abilities, and endurance, as they work to solve complex problems.
● This final round will determine the ultimate winners, showcasing the best talent in a highly competitive environment.
● Top 3 teams/individuals (maximum 3x3=9 head counts) will be declared as winners
Key Takeaways:
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
The Capture the Flag (CTF) challenge is expected to enhance cyber preparedness and encourage skill development among participants in the domain of cybersecurity. The event aims to identify and nurture talent capable of addressing real-world cyber threats through hands-on challenges. It will foster collaboration between defense personnel, ethical hackers, and young cyber enthusiasts, ultimately contributing to capacity building and strengthening the nation’s cyberdefense ecosystem.
Timeline
Announcement
July 12, 2025
Registration
July 12 - August 30, 2025
Shortlisting of participants
September 1 - 12, 2025
Grand Finale
September 17-18, 2025
Award Ceremony
October 7, 2025
All entries received will be shortlisted in the first round by a screening committee identified by NCRB & Cyber Peace Foundation.
The shortlisted entries from the first round, will be further evaluated in the final round by the Jury composed of experts from the field. The participants will be required to give a presentation (10 minutes) followed by Q&A (5mins). During this time, they will also be given the opportunity to share applications, investigative aids, videos, tools and proof of concept. The Jury will select best 3 entries as winners of Track 2 .
Regional Rounds
Pune, Kolkata, Udhampur, Lucknow and Chandigarh
August, 2025
August, 2025
Track 3
Datathon
The Datathon is a data-centric innovative challenge designed to test participants' ability to build robust technological solutions/models that can solve real-world problems related to defense and national security. This year’s focus is on predictive threat intelligence and anomaly detection using large-scale datasets
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Competition Format:
Registration:
Participants must register individually or in teams of up to three (3) team members and submit an abstract explaining their proposed approach to the challenge in PPT/PDF format. They should outline the problem they aim to solve, the methodology and technological stack they intend to use; and any relevant experience or past work in the specific domain.
Shortlisting
Participants/Teams will be evaluated based on the document/brief, the quality of their writeup/idea and PowerPoint presentation submitted during registrations on the basis of set judging criteria. Top 10 (maximum 10x3=30 head counts) Teams/Participants will be shortlisted for the Final Round
Grand Finale:
During the Grand Finale, shortlisted participants will have36 hours to transform their proposed solutions into working prototypes. They will have access to mentors and necessary resources to assist in the development process. Teams are encouraged to focus on functionality, usability, and scalability within the time limit. They may fine-tune their models or develop new ones in real time, presenting live solutions that are evaluated on performance, speed, precision, and scalability, as per mentors’ suggestions
Key Takeaways:
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
The Datathon track will provide participants with cutting-
edge problem statements at the intersection of AI, quantum computing, and cybersecurity. By working on challenges such as drone flight anomaly detection and quantum-enhanced malware and ransomware identification, participants are expected to develop advanced, real-time threat detection systems. This initiative aims to promote indigenous innovation in quantum machine learning, foster critical thinking, and generate prototype solutions that can strengthen national security and defense technology infrastructure
edge problem statements at the intersection of AI, quantum computing, and cybersecurity. By working on challenges such as drone flight anomaly detection and quantum-enhanced malware and ransomware identification, participants are expected to develop advanced, real-time threat detection systems. This initiative aims to promote indigenous innovation in quantum machine learning, foster critical thinking, and generate prototype solutions that can strengthen national security and defense technology infrastructure
Problem Statements:
1. Theme: Drone Flight Anomaly Detection
a. Objective: Build a predictive model to detect anomalies in drone flight paths using telemetry data.
b. Dataset: Time-series logs of altitude, velocity, yaw, pitch, battery, and GPS drift.
c. Challenge: Identify malfunction patterns, signal jamming, or unauthorized diversions.
d. Impact: Prevent mid-air mission failures or hijack scenarios during surveillance ops.
a. Objective: Build a predictive model to detect anomalies in drone flight paths using telemetry data.
b. Dataset: Time-series logs of altitude, velocity, yaw, pitch, battery, and GPS drift.
c. Challenge: Identify malfunction patterns, signal jamming, or unauthorized diversions.
d. Impact: Prevent mid-air mission failures or hijack scenarios during surveillance ops.
2. Theme: Quantum Machine Learning for Threat Detection
○ Quantum Malware Hunter
Problem: Use quantum-enhanced ML to detect unknown malware patterns in real-time.
Dataset: Simulated network traffic or malware signatures.
○ AI + Quantum = Early Ransomware Detection
Problem: Build a hybrid AI system (classical +quantum) that predicts ransomware deploymentpatterns from user behavior logs.
○ Quantum Malware Hunter
Problem: Use quantum-enhanced ML to detect unknown malware patterns in real-time.
Dataset: Simulated network traffic or malware signatures.
○ AI + Quantum = Early Ransomware Detection
Problem: Build a hybrid AI system (classical +quantum) that predicts ransomware deploymentpatterns from user behavior logs.
Timeline
Announcement
July 16, 2025
Registration
July 12 - August 30, 2025
Shortlisting of participants
September 1 - 12, 2025
Grand Finale
September 19-20, 2025
Award Ceremony
October 7, 2025
All entries received will be shortlisted in the first round by a screening committee identified by NCRB & Cyber Peace Foundation.
The shortlisted entries from the first round, will be further evaluated in the final round by the Jury composed of experts from the field. The participants will be required to give a presentation (10 minutes) followed by Q&A (5mins). During this time, they will also be given the opportunity to share applications, investigative aids, videos, tools and proof of concept. The Jury will select best 3 entries as winners of Track 2 .
Regional Rounds
Pune, Kolkata, Udhampur, Lucknow and Chandigarh
August, 2025
August, 2025
CCTNS Scheme had been conceptualized as a comprehensive and integrated system for enhancing the efficiency and effective policing at all levels and especially at the Police Station level in order to achieve the following key objectives:
Creating Centralized Databases
Creating State and Central levels databases on crime and criminals starting from FIRs.
Sharing Real-time Information
Enable easy sharing of real-time information/ intelligence across police stations, districts and States.
Prevention
Improved investigation and crime prevention.
Citizen Portals
Improved service delivery to the public/ stakeholders through Citizen Portals.
No. of participants
0+
Awards
0+
Tracks
0+
Cities
0+
Important Dates
16
July
30
August
17-18
September
Shortlisting
period
period
07
October
Registration
Starts
Starts
Registration
Closes
Closes
Final
Round
Round
Award Ceremony
Judging Criteria:
A panel of experts will be formed by the Territorial Army and CyberPeace.
The Judging Criteria as follows-
The Judging Criteria as follows-
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Innovation & Relevance to the Theme
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The solution must align well with the competition's theme and directly address the selected problem statement. The idea should demonstrate uniqueness, creativity, or innovation in its approach
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Feasibility of the Solution
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The solution should be practical and realistic to implement in real-world scenarios. It must be technically and economically viable, with resources or infrastructure being reasonably accessible. The potential challenges of implementing the solution should be identified, and appropriate strategies to address them must be outlined effectively
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Illustration of the Idea
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The idea must be clearly presented, highlighting key features, workflows, and functionality. Visual aids like diagrams, flowcharts, or lifecycle visualizations should be used effectively
Grand Finale
Weightage:
Weightage:
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Technical Depth
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
Participants need to mention the coding and technologies or any special framework, libraries they have used. For any coding sample it is to be checked if the code sample is well-commented that anyone can understand what is the usability of that particular piece of code
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Criteria:
Presentation & Demonstration
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
● Detection: Use advanced AI and machine learning methods to detect deepfakes with high accuracy while reducing false positives and negatives.
● Real-time Analysis: Process and analyze media information in real or near real time to ensure early detection of deepfakes, particularly in key settings such as live news broadcasts or social media platforms.
● Mitigation: Create ways to reduce the impact of recognized deepfakes, such as watermarking legitimate content, notifying users or platforms, and giving verifiable evidence to dispute bogus information.
● Scalability and Efficiency: Make sure the solution is scalable and can be integrated into several platforms (social media, news outlets, and government organizations) without sacrificing performance.
● Ethical considerations: Address ethical concerns about privacy, data consumption, and the possible misuse of the detection system. The solution should also follow legal guidelines and be flexible to changing requirements.
Objectives:
● Accuracy: Maintain a high detection accuracy rate across various media formats (video, audio, and pictures).
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
● Speed: Ensure that the system can process and analyze media content rapidly and with low latency.
● User Interface: Create an easy-to-use interface for cybersecurity professionals, content moderators, and law enforcement.
● Reporting: When a deepfake is found, send detailed reports or alerts that include confidence scores and the nature of the manipulation.
Description:
The PowerPoint presentation should be clear, organized, and comprehensive. It must effectively convey the idea using a balanced mix of visuals, text, and explanations to ensure the audience understands the solution
Key Highlights:
Medallions and Certificates:
Awarded by the Territorial Army, these will serve as a prestigious recognition for participants' contributions and achievements during the event.
Attractive Prizes:
Winners will receive exciting prizes, enhancing the competitive spirit and rewarding innovative solutions that can contribute to national security
Boarding and Lodging:
All participants will be provided with comfortable boarding and lodging facilities for the duration of the event, ensuring a seamless experience.
Opportunity to Contribute to the Territorial Army:
Participants will have a rare chance to collaborate with the Territorial Army, working alongside defense professionals and contributing directly to the national defense landscape
Honoring of Winners:
Winners of each event will receive the ultimate recognition by being honored by the Chief of Army Staff (COAS), Indian Army, one of the most prestigious accolades in the defense and cybersecurity space
Special Recognition Awards
Special Attraction: Awards will be presented by the Hon’ble Prime Minister / Hon’ble Raksha Mantri
Join Us
Contribute to the future of the National Territorial Army’s defense technology! Register now to participate in Terrier Cyber Quest 2025 and turn your innovative ideas into reality.
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Hall of Fame
In the CyberChallenge Hall of Fame, we honour the achievements of top cybersecurity champions. Join us in celebrating their accomplishments and commitment to securing our nation's cyber infrastructure.
Explore the profiles of our winners from previous years.
Explore the profiles of our winners from previous years.
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