Implementation of Robust Deepfake Detection via 3D Residual Attention Densenet with Pyramid Dilation Network

Artificial intelligence is used in deepfake technology to produce realistic-looking but fake films, audios, and videos. The popularity and use of videos have increased dramatically in recent years due to the proliferation of social networks and mobile devices. In recent days, most of the high-budget movies were accompanied by intelligent techniques and tools for video manipulation, such as FaceSwap and Deepfake. In live streaming, deepfakes frequently use context-dependent changes that test the flexibility of models that were previously trained on static datasets. The complexities of live-streaming deepfakes may be too complex for traditional methods to fully capture. The validity of visual content is seriously threatened by deepfake technology, especially in live-stream situations when detection speed is critical. Due to the shortcomings and difficulties of current Deepfake detection techniques, more reliable and accurate solutions are required. In this work, advanced deep learning approaches are utilized for detecting deepfake videos to enhance security and trust in digital media. The necessary videos for the deepfake detection are collected from the standard datasets. The gathered video sequences are processed through the proposed deep learning model named as 3D Residual Attention Densenet with Pyramid Dilation (3DRAD-PD). The designed approach can capture multi-scale context information from input videos to improve the accuracy of deepfake detection. It helps to identify the manipulated videos to avoid the spread of misinformation in social media. Extensive experiments are conducted to demonstrate the robustness of the designed framework against different types of deepfake videos.