In this paper we present TruFor, a forensic framework that can be applied to a large variety of image manipulation methods, from classic cheapfakes to more recent manipulations based on deep learning. We rely on the extraction of both high-level and low-level traces through a transformer-based fusion architecture that combines the RGB image and a learned noise-sensitive fingerprint. The latter learns to embed the artifacts related to the camera internal and external processing by training only on real data in a self-supervised manner. Forgeries are detected as deviations from the expected regular pattern that characterizes each pristine image. Looking for anomalies makes the approach able to robustly detect a variety of local manipulations, ensuring generalization. In addition to a pixel-level localization map and a whole-image integrity score, our approach outputs a reliability map that highlights areas where localization predictions may be error-prone. This is particularly important in forensic applications in order to reduce false alarms and allow for a large scale analysis. Extensive experiments on several datasets show that our method is able to reliably detect and localize both cheapfakes and deepfakes manipulations outperforming state-of-the-art works. Code is publicly available at https://grip-unina.github.io/TruFor/

翻译：本文提出TruFor，一个可应用于多种图像篡改方法的取证框架，涵盖从经典廉价伪造到基于深度学习的新近篡改技术。我们通过基于Transformer的融合架构提取高层与低层痕迹，该架构结合RGB图像与经学习的噪声敏感指纹。后者通过仅在真实数据上进行自监督训练，学习嵌入与相机内部及外部处理相关的伪影。伪造行为被检测为偏离每张原始图像预期正则模式的特征。通过寻找异常，该方法能够稳健地检测多种局部篡改，确保泛化能力。除像素级定位图和整图完整性评分外，本方法还输出可靠性图，突出显示定位预测可能出错的区域。这在取证应用中尤为重要，可减少误报并支持大规模分析。在多个数据集上的大量实验表明，本方法能够可靠检测与定位廉价伪造及深度伪造篡改，性能超越现有最先进工作。代码已开源发布于https://grip-unina.github.io/TruFor/。