Deep Fake Detection with A Unified Discrepancy-Aware Forgery Detection Network and Attention-Guided Feature Rectification

Deepfake technology, which allows the creation of manipulated, yet highly realistic, content have made it difficult to ascertain the integrity of any form of digital media. In order to solve this problem, we introduce an end-to-end deep-learned framework called Discrepancy-Aware Forgery Detection Network (DAFDN) dedicated to the task of detecting forged media to tackle representation biases and capture irregular patterns in forgery samples. This consists of a Feature Representation Extractor (FRE) and a Feature Refinement Module (FRM), and both jointly generates representative but not biased feature representations. In addition, an Attention-Guided Feature Rectification (AGFR) mechanism is adopted to both combine and refine features, and the Discrepancy-Aware Interaction Module (DAIM) explores the manipulation clues through regional and channel-level discrepancy. To improve detection, the framework uses Region-Aware Forgery Detection (RAFD) by spatial analysis and Channel Discrepancy Analysis (CDA) by channel-wise exploration. Utilizing data, no later than October 2023, our method surpasses state-of-the-art methods under challenging datasets such as Celeb-DF, WildDeepfake, and DFDC, indicating our success in detecting minute manipulations. This research significantly enhances deepfake detection by utilizing sophisticated techniques to fine-tune representations and exploit differences.