Revealing GAN-generated faces through local camera surface frame analysis

The ability of AI to generate highly realistic, fully synthetic images, particularly of human faces, is rapidly advancing, making it increasingly difficult to distinguish between real and artificially generated content. This growing realism highlights the urgent need for reliable methods to detect subtle inconsistencies introduced during the image generation process. A fundamental distinction between authentic and deepfake content lies in the absence, for the latter, of an acquisition process by a real camera. As a result, the intricate relationships among scene elements, such as lighting, reflectance, and spatial positioning, are not captured from the physical world but are artificially reconstructed. Motivated by this observation, we propose the use of local camera surface frames as a feature to encode such environment-specific attributes. Our experimental results demonstrate that this representation not only achieves high detection accuracy but also exhibits strong and robust generalisation capabilities across different GAN-based generative models.