Research Projects

The rapid advancement in deepfake technologies presents a significant challenge to digital security, trust, and privacy. This project aims to develop state-of-the-art deepfake detection architectures that can effectively identify manipulations in text, audio, and video content, both individually and in combination. The primary focus will be on addressing the unique challenges posed by deepfakes in the context of Indian ethnicities, considering the diverse linguistic, cultural, and visual nuances. This project will enhance digital security and trust by providing reliable tools to detect deepfakes. It will also promote awareness and education on the ethical implications of deepfake technology, ensuring a safer and more informed digital ecosystem. By focusing on the specific challenges related to Indian ethnicities, the project aims to bridge the gap in current deepfake detection technologies and provide solutions that are both culturally aware and technically advanced. Scientific Rationale: Especially with a focus on Indian ethnicities, is grounded in several key factors: 1. Advancement of AI and Deepfake Technologies 2. Multimodal Analysis 3. Cultural and Linguistic Diversity 4. Data Collection and Representation 5. Ethical and Social Implications 6. Technological Integration 7. Continuous Learning and Adaptation In summary, the scientific rationale for this project lies in the need to keep pace with advancing deepfake technologies, address the unique challenges posed by India's diversity, and develop ethical and robust detection systems that protect against the misuse of synthetic media. By leveraging cutting-edge AI techniques and focusing on multimodal analysis, the project aims to create comprehensive and effective deepfake detection architectures. Experiments: To develop and validate state-of-the-art deepfake detection architectures, the project will involve a series of main experiments across text, audio, video, and multi-modal deepfake detection. Here's an overview: 1. Text Deepfake Detection Experiments 1.1 Synthetic Text Generation and Detection 1.2 Contextual Analysis 1.3 Cross-Language Performance 2. Audio Deepfake Detection Experiments 2.1 Audio Data Collection and Augmentation 2.2 Vocal Pattern Analysis 2.3 Speaker Verification and Voice Cloning Detection 3. Video Deepfake Detection Experiments 3.1 Facial Feature Analysis 3.2 Audio-Visual Synchronization 3.3 Temporal Coherence Checks 4. Multi-modal Deepfake Detection Experiments 4.1 Integrated Detection Framework 4.2 Cross-Modal Correlation Analysis 4.3 Multi-Modal Dataset Collection 4.4 Continuous Learning Mechanisms: 5. Performance Evaluation and Benchmarking 5.1 Accuracy and Robustness Metrics 5.2 Benchmarking Against State-of-the-Art Methods 5.3 Real-World Scenario Testing These experiments aim to develop comprehensive and robust deepfake detection architectures that address the unique challenges posed by the Indian context, enhancing digital security and trust.