[2603.29356] CIPHER: Counterfeit Image Pattern High-level Examination via Representation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.29356 (cs) [Submitted on 31 Mar 2026] Title:CIPHER: Counterfeit Image Pattern High-level Examination via Representation Authors:Kyeonghun Kim, Youngung Han, Seoyoung Ju, Yeonju Jean, YooHyun Kim, Minseo Choi, SuYeon Lim, Kyungtae Park, Seungwoo Baek, Sieun Hyeon, Nam-Joon Kim, Hyuk-Jae Lee View a PDF of the paper titled CIPHER: Counterfeit Image Pattern High-level Examination via Representation, by Kyeonghun Kim and 11 other authors View PDF HTML (experimental) Abstract:The rapid progress of generative adversarial networks (GANs) and diffusion models has enabled the creation of synthetic faces that are increasingly difficult to distinguish from real images. This progress, however, has also amplified the risks of misinformation, fraud, and identity abuse, underscoring the urgent need for detectors that remain robust across diverse generative models. In this work, we introduce Counterfeit Image Pattern High-level Examination via Representation(CIPHER), a deepfake detection framework that systematically reuses and fine-tunes discriminators originally trained for image generation. By extracting scale-adaptive features from ProGAN discriminators and temporal-consistency features from diffusion models, CIPHER captures generation-agnostic artifacts that conventional detectors often overlook. Through extensive experiments across nine state-of-the-art generative models, CIPHER demonstrates superior cross-model de...