Digital image forensics plays a crucial role in image authentication and manipulation localization. Despite the progress powered by deep neural networks, existing forgery localization methodologies exhibit limitations when deployed to unseen datasets and perturbed images (i.e., lack of generalization and robustness to real-world applications). To circumvent these problems and aid image integrity, this paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. The rationale is grounded on the observation that most image signal processors (ISP) involve the demosaicing process, which introduces pixel correlations in pristine images. Moreover, manipulating operations, including splicing, copy-move, and inpainting, directly affect such pixel regularity. We, therefore, first split the input image into several blocks and design masked self-attention mechanisms to model the global pixel dependency in input images. Simultaneously, we optimize another local pixel dependency stream to mine local manipulation clues within input forgery images. In addition, we design novel Learning-to-Weight Modules (LWM) to combine features from the two streams, thereby enhancing the final forgery localization performance. To improve the training process, we propose a novel Pixel-Inconsistency Data Augmentation (PIDA) strategy, driving the model to focus on capturing inherent pixel-level artifacts instead of mining semantic forgery traces. This work establishes a comprehensive benchmark integrating 15 representative detection models across 12 datasets. Extensive experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints and achieve state-of-the-art generalization and robustness performances in image manipulation localization.

翻译：数字图像取证在图像认证和篡改定位中扮演关键角色。尽管深度神经网络推动了相关进展，现有伪造定位方法在部署到未见数据集和受扰动图像时仍表现出局限性（即缺乏对实际应用的泛化性和鲁棒性）。为解决这些问题并保障图像完整性，本文通过分析像素不一致性伪影，提出了一种兼具泛化性和鲁棒性的篡改定位模型。其理论依据在于：大多数图像信号处理器（ISP）涉及去马赛克过程，该过程会在原始图像中引入像素相关性。而拼接、复制-移动和修复等篡改操作会直接影响这种像素规律。为此，我们首先将输入图像分割为多个块，并设计掩码自注意力机制以建模输入图像中的全局像素依赖关系。同时，我们优化另一条局部像素依赖流，以挖掘输入伪造图像中的局部篡改线索。此外，我们设计了新颖的学习加权模块（LWM）来融合两流的特征，从而提升最终伪造定位性能。为改进训练过程，我们提出了一种像素不一致性数据增强（PIDA）策略，驱动模型专注于捕获固有的像素级伪影，而非挖掘语义级伪造痕迹。本研究建立了涵盖12个数据集、15个代表性检测模型的综合基准。大量实验表明，我们的方法能成功提取固有的像素不一致性伪造指纹，在图像篡改定位任务中达到最先进的泛化性和鲁棒性表现。