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.

提出了一种基于 Transformers 的图像伪造定位方法 TransForensics，结合密集自注意力编码器和密集校正模块用于模拟全局上下文和不同尺度下的本地补丁之间的所有成对相互作用，该方法不仅可以捕捉有区分性的表示并获得高质量的蒙版预测，而且不受篡改类型和补丁序列顺序的限制，在主要基准测试中表现优于现有的现有方法。