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Skill2Career3 days left
Entry
Free
Organized by
International Institute of Information Technology (IIIT), Bangalore
Identify Synthetic Media, Train Robust ML Models, and Safeguard Digital Integrity! About the Event Dive into the cutting edge of AI ethics and security in this high-intensity machine learning hackathon focused on Deepfake Detection ! Participants will compete to build accurate, efficient, and robust models designed to identify synthetic media. Designed to test both technical prowess and ethical AI thinking, this single-round challenge demands originality and precision as you train and optimize models against a provided custom dataset. Submission Guidelines Submissions must be provided via a Google Drive folder link (set to "Anyone with the link can view" ) containing: Source Code: Zipped code directory or a public GitHub repository link with a comprehensive README.md . Trained Model Weights: Model files compatible with PyTorch or TensorFlow . Prediction CSV: Model outputs generated on the official test set. Detailed Documentation: Covering your approach, model architecture, hyperparameter selection, training pipeline, and challenges faced. Explanatory Video (Optional): A brief 3–5 minute video demonstrating your solution and methodology. Rules & Regulations Dataset Restrictions: Only the officially provided dataset may be used for training. External training datasets are strictly prohibited. Pre-trained Models: Pre-trained architectures are p…
Identify Synthetic Media, Train Robust ML Models, and Safeguard Digital Integrity! About the Event Dive into the cutting edge of AI ethics and security in this high-intensity machine learning hackathon focused on Deepfake Detection ! Participants will compete to build accurate, efficient, and robust models designed to identify synthetic media. Designed to test both technical prowess and ethical AI thinking, this single-round challenge demands originality and precision as you train and optimize models against a provided custom dataset. Submission Guidelines Submissions must be provided via a Google Drive folder link (set to "Anyone with the link can view" ) containing: Source Code: Zipped code directory or a public GitHub repository link with a comprehensive README.md . Trained Model Weights: Model files compatible with PyTorch or TensorFlow . Prediction CSV: Model outputs generated on the official test set. Detailed Documentation: Covering your approach, model architecture, hyperparameter selection, training pipeline, and challenges faced. Explanatory Video (Optional): A brief 3–5 minute video demonstrating your solution and methodology. Rules & Regulations Dataset Restrictions: Only the officially provided dataset may be used for training. External training datasets are strictly prohibited. Pre-trained Models: Pre-trained architectures are p…
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Registration closes
Wed, 28 October 2026
2 days left
Hacking begins
Wed, 28 October 2026
Submissions close
Wed, 28 October 2026
Total prize pool
₹20K
₹20,000 total
Who can enter
Students, Freshers, Corporates
Team size
1–4 members per team
Skill2Career3 days left
Entry
Free
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Prize pool
₹90K
Quality Thought Info SystemsCloses tomorrow
Entry
Free
₹20K prize pool
2 days left