Detecting GANs and Retouching Based Digital Alterations via DAD-HCNN

Anubhav Jain, Puspita Majumdar, Richa Singh, Mayank Vatsa; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 672-673

While image generation and editing technologies such as Generative Adversarial Networks and Photoshop are being used for creative and positive applications, the misuse of these technologies to create negative applications including Deep-nude and fake news is also increasing at a rampant pace. Therefore, detecting digitally created and digitally altered images is of paramount importance. This paper proposes a hierarchical approach termed as DAD-HCNN which performs two-fold task: (i) it differentiates between digitally generated images and digitally retouched images from the original unaltered images, and (ii) to increase the explainability of the decision, it also identifies the GAN architecture used to create the image. The effectiveness of the model is demonstrated on a database generated by combining face images generated from four different GAN architectures along with the retouched images and original images from existing benchmark databases.