Advanced image tampering techniques are increasingly challenging the trustworthiness of multimedia, leading to the development of Image Manipulation Localization (IML). But what makes a good IML model? The answer lies in the way to capture artifacts. Exploiting artifacts requires the model to extract non-semantic discrepancies between the manipulated and authentic regions, which needs to compare differences between these two areas explicitly. With the self-attention mechanism, naturally, the Transformer is the best candidate. Besides, artifacts are sensitive to image resolution, amplified under multi-scale features, and massive at the manipulation border. Therefore, we formulate the answer to the former question as building a ViT with high-resolution capacity, multi-scale feature extraction capability, and manipulation edge supervision. We term this simple but effective ViT paradigm as the IML-ViT, which has great potential to become a new benchmark for IML. Extensive experiments on five benchmark datasets verified our model outperforms the state-of-the-art manipulation localization methods. Code and models are available at \url{https://github.com/SunnyHaze/IML-ViT}

翻译：高级图像篡改技术日益挑战多媒体的可信度，推动了图像篡改定位（IML）领域的发展。但什么才是好的IML模型？答案在于捕获伪影的方式。利用伪影要求模型提取篡改区域与真实区域之间的非语义差异，这需要显式比较这两个区域的差异。凭借自注意力机制，Transformer自然是最佳选择。此外，伪影对图像分辨率敏感，在多尺度特征下被放大，并在篡改边界处大量聚集。因此，我们将前述问题的答案归纳为构建具有高分辨率能力、多尺度特征提取能力和篡改边缘监督能力的ViT。我们将这种简单而有效的ViT范式命名为IML-ViT，它有望成为IML领域的新基准。在五个基准数据集上的大量实验验证了我们的模型优于最先进的篡改定位方法。代码和模型可在\url{https://github.com/SunnyHaze/IML-ViT}获取。