Hybrid framework for image forgery detection and robustness against adversarial attacks using vision transformer and SVM

Abstract

People routinely capture photos and videos to document their daily experiences, with such visual media frequently regarded as reliable sources of evidence. The proliferation of social networking platforms, digital photography technologies, and image manipulation applications have introduced emerging concerns that demand investigation by academics, industry executives, and cybersecurity experts. These concerns specifically relate to identifying and mitigating fraudulent visual content across online platforms. The deliberate alteration of photographs and videos has become progressively prevalent, potentially resulting in severe emotional, bodily, and societal damage to affected persons. This research introduces a combined Deep Learning approach utilizing a pre-trained Vision Transformer (ViT) for feature extraction alongside Support Vector Machine (SVM) for dual-category image classification, differentiating authentic from manipulated photographs (Copy-move & Splicing). Additionally, we implemented adversarial training techniques to enhance model robustness against adversarial attacks. The introduced approach underwent comprehensive evaluation across multiple benchmarks, including CASIA v1.0, CASIA v2.0, MICC-F220, MICC-F2000, and MICC-F600. The methodology exhibits considerable potential regarding forgery detection performance following extensive validation. The proposed framework demonstrated competitive performance and improved robustness against image manipulations compared to existing methods in manipulation detection tasks.