TransForensics: Image Forgery Localization with Dense Self-Attention

TL;DR

TransForensics is a Transformer-based image forgery localization method that models global context and improves detection accuracy across tampering types, outperforming existing methods on benchmark datasets.

Contribution

Introduces TransForensics, a novel Transformer-inspired framework with dense self-attention and correction modules for robust image forgery localization.

Findings

Outperforms state-of-the-art methods on benchmarks

Captures discriminative features effectively

Not limited by tampering types or patch order

Abstract

Nowadays advanced image editing tools and technical skills produce tampered images more realistically, which can easily evade image forensic systems and make authenticity verification of images more difficult. To tackle this challenging problem, we introduce TransForensics, a novel image forgery localization method inspired by Transformers. The two major components in our framework are dense self-attention encoders and dense correction modules. The former is to model global context and all pairwise interactions between local patches at different scales, while the latter is used for improving the transparency of the hidden layers and correcting the outputs from different branches. Compared to previous traditional and deep learning methods, TransForensics not only can capture discriminative representations and obtain high-quality mask predictions but is also not limited by tampering types and patch sequence orders. By conducting experiments on main benchmarks, we show that TransForensics outperforms the stateof-the-art methods by a large margin.