RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images
With the rapid development of the image generation technologies, the malicious abuses of the GAN-generated fingerprint images poses a significant threat to the public safety in certain circumstances. Although the existing universal deep forgery detection approach can be applied to detect the fake fingerprint images, they are easily attacked and have poor robustness. Meanwhile, there is no specifically designed deep forgery detection method for fingerprint images. In this paper, we propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images, to the best of our knowledge. Specifically, we firstly construct a ridge stream, which exploits the grayscale variations along the ridges to extract unique fingerprint-specific features. Then, we construct a generation artifact stream, in which the FFT-based spectrums of the input fingerprint images are exploited, to extract more robust generation artifact features. At last, the unique ridge features and generation artifact features are fused for binary classification (i.e., real or fake). Comprehensive experiments demonstrate that our proposed approach is effective and robust with low complexities.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationImage GenerationSimilar Papers 제목 키워드 기반
Decoupling Semantics and Fingerprints: A Universal Representation for AI-Generated Image Detection
Detecting AI-generated images across unseen architectures remains challenging, as existing models often overfit to generator-specific fingerprints and semantic content rather than learning universal forgery traces. We at…
Comprint: Image Forgery Detection and Localization using Compression Fingerprints
Manipulation tools that realistically edit images are widely available, making it easy for anyone to create and spread misinformation. In an attempt to fight fake news, forgery detection and localization methods were des…
Image Forgery DetectionMisinformationCausal Fingerprints of AI Generative Models
AI generative models leave implicit traces in their generated images, which are commonly referred to as model fingerprints and are exploited for source attribution. Prior methods rely on model-specific cues or synthesis …
Smudged Fingerprints: A Systematic Evaluation of the Robustness of AI Image Fingerprints
Model fingerprint detection has shown promise to trace the provenance of AI-generated images in forensic applications. However, despite the inherent adversarial nature of these applications, existing evaluations rarely c…
D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization
Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the local patches or local objects. In fact…
Binary Classification