RAW Data: A Key Component for Effective Deepfake Detection

Abstract

Current deepfake detection methods are prone to overfitting to specific deepfake artifacts and often struggle with genuine images that have undergone compression and other image processing operations. These processes can obscure indicators of forgery, leading to inaccurate decisions. This paper aims to redefine the boundary between real and fake images by narrowing the definition of authentic samples to a stage closer to the radiance of the scene as captured by the sensor, prior to any transformations by an Image Signal Processor (ISP). Our proposed method bypasses ISP processing steps, such as denoising, white balance, and demosaicing, which are embedded in camera hardware. This unaltered preservation makes raw data an ideal starting point for deepfake detection. Given the scarcity of large-scale datasets designed for training on raw images, we propose a methodological approach to train our model on raw image data. Our method demonstrated state-of-the-art performance on the CDF dataset and showed competitive results across other RGB domain deepfake detection datasets. The model developed in this study is available at https://github.com/DeepFaux/Deepfake-Detection-with-RAW-Data.