Self-supervised GAN Detector

Abstract

Although the recent advancement in generative models brings diverse advantages to society, it can also be abused with malicious purposes, such as fraud, defamation, and fake news. To prevent such cases, vigorous research is conducted to distinguish the generated images from the real images, but challenges still remain to distinguish the unseen generated images outside of the training settings. Such limitations occur due to data dependency arising from the model's overfitting issue to the training data generated by specific GANs. To overcome this issue, we adopt a self-supervised scheme to propose a novel framework. Our proposed method is composed of the artificial fingerprint generator reconstructing the high-quality artificial fingerprints of GAN images for detailed analysis, and the GAN detector distinguishing GAN images by learning the reconstructed artificial fingerprints. To improve the generalization of the artificial fingerprint generator, we build multiple autoencoders with different numbers of upconvolution layers. With numerous ablation studies, the robust generalization of our method is validated by outperforming the generalization of the previous state-of-the-art algorithms, even without utilizing the GAN images of the training dataset.

References in corpus (10)

• mixup: Beyond Empirical Risk Minimization
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
• Exposing DeepFake Videos By Detecting Face Warping Artifacts
• Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries
• ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection
• Unmasking DeepFakes with simple Features
• KoDF: A Large-scale Korean DeepFake Detection Dataset
• T-GD: Transferable GAN-generated Images Detection Framework
• Generalized Zero and Few-Shot Transfer for Facial Forgery Detection