Unsupervised Generative Fake Image Detector

Abstract

Recently, the rapid advancement of generative model has led to its exploitation by malicious actors who employ it to fabricate fake synthetic images. Meanwhile, the deceptive images are often disseminated on social network platforms, thereby undermining public trust. Although reliable forensic tools have emerged to detect generative fake images, the existing supervised detectors excessively rely on the correctly-labeled training samples, leading to overwhelming outsourcing annotation costs and the potential risk of suffering from label flipping attack. In light of the aforementioned limitations, we propose an unsupervised detector fighting against generative fake image. In particular, we assign the noisy labels to the training samples. Then dependent on the pre-clustered samples with noisy labels, the strategy of pre-training and re-training mechanism helps us train the feature extractor utilized to extract the discriminative feature. Last, the extracted feature guides us to respectively cluster both pristine and fake images; the fake images are effectively filtered by employing cosine similarity. Extensive experimental results highlight that our unsupervised detector rivals the baseline supervised methods; moreover, it has better capability of defending against label flipping attack.