Deepfake Detection CNN Development
Budget: ₹600 – ₹1,500 INR
I require a purpose-built deep learning model capable of reliably distinguishing authentic images from manipulated (deepfake) images. The scope is tightly focused on model design, training, and evaluation: developing an effective CNN-based architecture, training it on established deepfake datasets, and tuning it to perform robustly under real-world conditions.
The implementation may use TensorFlow / Keras, PyTorch, or an equivalent framework, provided the entire training and inference pipeline is fully reproducible on a single modern GPU. I can supply standard datasets (e.g., FaceForensics++ image frames, DFDC samples) along with additional proprietary images if required. Please indicate if alternative datasets would materially improve performance.
Deliverables
• Well-documented source code with clear modular structure
• requirements.txt (or equivalent) for environment reproduction
• Trained model weights
• A concise README detailing dataset preparation, training commands, and inference steps
• Technical evaluation report including:
Accuracy
Precision / Recall
F1-score
ROC-AUC
Confusion matrix on a held-out test split
Acceptance Criteria
• ≥ 90% balanced accuracy on the provided test dataset
• Inference latency ≤ 50 ms per image on an RTX 3080–class GPU
• Model generalizes beyond training data (no dataset leakage or overfitting artifacts)
Additional Notes
If you have prior experience in deepfake detection, face forgery analysis, or media forensics, please include a short note or reference to past work (paper, repository, or deployed system). This will help assess technical fit efficiently.
The implementation may use TensorFlow / Keras, PyTorch, or an equivalent framework, provided the entire training and inference pipeline is fully reproducible on a single modern GPU. I can supply standard datasets (e.g., FaceForensics++ image frames, DFDC samples) along with additional proprietary images if required. Please indicate if alternative datasets would materially improve performance.
Deliverables
• Well-documented source code with clear modular structure
• requirements.txt (or equivalent) for environment reproduction
• Trained model weights
• A concise README detailing dataset preparation, training commands, and inference steps
• Technical evaluation report including:
Accuracy
Precision / Recall
F1-score
ROC-AUC
Confusion matrix on a held-out test split
Acceptance Criteria
• ≥ 90% balanced accuracy on the provided test dataset
• Inference latency ≤ 50 ms per image on an RTX 3080–class GPU
• Model generalizes beyond training data (no dataset leakage or overfitting artifacts)
Additional Notes
If you have prior experience in deepfake detection, face forgery analysis, or media forensics, please include a short note or reference to past work (paper, repository, or deployed system). This will help assess technical fit efficiently.