Published Paper Details:

In the realm of digital media, the proliferation of deepfake technology has brought forth significant concerns regarding its potential for misuse, particularly in areas such as political disinformation and privacy infringement. To counter these threats, we propose a groundbreaking approach to deepfake analysis that encompasses both image and audio detection. This method integrates advanced techniques such as Res-Next CNN for precise frame-level feature extraction from images and MFCC feature extraction for audio, coupled with LSTM-based RNN for comprehensive temporal analysis across both modalities. This model exhibits a remarkable ability to accurately discern between authentic content and deepfakes in both images and audio recordings. Additionally, our methodology emphasizes adaptability and scalability, ensuring its effectiveness across various digital platforms and evolving deepfake techniques in both visual and auditory domains. By continuously refining our model with ongoing advancements in AI and deep learning, we remain steadfast in our commitment to staying ahead of emerging threats posed by malicious actors. In addition to its utility in detecting deepfakes across multiple modalities, our system features a user-friendly interface for swift mitigation of AI-generated manipulations. By leveraging the power of AI to combat AI, our methodology prioritizes simplicity and reliability, validated through rigorous evaluations on diverse datasets encompassing both image and audio samples. Through these efforts, we safeguard against the harmful impact of deepfakes, thus preserving the integrity of digital media and protecting individuals' privacy and societal well-being.