Zhuohang Jiang

Xiaochen Ma, Jizhe Zhou, Xiong Xu et al.

Nowadays, multimedia forensics faces unprecedented challenges due to the rapid advancement of multimedia generation technology thereby making Image Manipulation Localization (IML) crucial in the pursuit of truth. The key to IML lies in revealing the artifacts or inconsistencies between the tampered and authentic areas, which are evident under pixel-level features. Consequently, existing studies treat IML as a low-level vision task, focusing on allocating tampered masks by crafting pixel-level features such as image RGB noises, edge signals, or high-frequency features. However, in practice, tampering commonly occurs at the object level, and different classes of objects have varying likelihoods of becoming targets of tampering. Therefore, object semantics are also vital in identifying the tampered areas in addition to pixel-level features. This necessitates IML models to carry out a semantic understanding of the entire image. In this paper, we reformulate the IML task as a high-level vision task that greatly benefits from low-level features. Based on such an interpretation, we propose a method to enhance the Masked Autoencoder (MAE) by incorporating high-resolution inputs and a perceptual loss supervision module, which is termed Perceptual MAE (PMAE). While MAE has demonstrated an impressive understanding of object semantics, PMAE can also compensate for low-level semantics with our proposed enhancements. Evidenced by extensive experiments, this paradigm effectively unites the low-level and high-level features of the IML task and outperforms state-of-the-art tampering localization methods on all five publicly available datasets.

Xiaochen Ma, Xuekang Zhu, Lei Su et al.

A comprehensive benchmark is yet to be established in the Image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely undermining the development of this field. However, the scarcity of open-sourced baseline models and inconsistent training and evaluation protocols make conducting rigorous experiments and faithful comparisons among IMDL models challenging. To address these challenges, we introduce IMDL-BenCo, the first comprehensive IMDL benchmark and modular codebase. IMDL-BenCo: i) decomposes the IMDL framework into standardized, reusable components and revises the model construction pipeline, improving coding efficiency and customization flexibility; ii) fully implements or incorporates training code for state-of-the-art models to establish a comprehensive IMDL benchmark; and iii) conducts deep analysis based on the established benchmark and codebase, offering new insights into IMDL model architecture, dataset characteristics, and evaluation standards. Specifically, IMDL-BenCo includes common processing algorithms, 8 state-of-the-art IMDL models (1 of which are reproduced from scratch), 2 sets of standard training and evaluation protocols, 15 GPU-accelerated evaluation metrics, and 3 kinds of robustness evaluation. This benchmark and codebase represent a significant leap forward in calibrating the current progress in the IMDL field and inspiring future breakthroughs. Code is available at: https://github.com/scu-zjz/IMDLBenCo.

Xuekang Zhu, Xiaochen Ma, Lei Su et al.

The mesoscopic level serves as a bridge between the macroscopic and microscopic worlds, addressing gaps overlooked by both. Image manipulation localization (IML), a crucial technique to pursue truth from fake images, has long relied on low-level (microscopic-level) traces. However, in practice, most tampering aims to deceive the audience by altering image semantics. As a result, manipulation commonly occurs at the object level (macroscopic level), which is equally important as microscopic traces. Therefore, integrating these two levels into the mesoscopic level presents a new perspective for IML research. Inspired by this, our paper explores how to simultaneously construct mesoscopic representations of micro and macro information for IML and introduces the Mesorch architecture to orchestrate both. Specifically, this architecture i) combines Transformers and CNNs in parallel, with Transformers extracting macro information and CNNs capturing micro details, and ii) explores across different scales, assessing micro and macro information seamlessly. Additionally, based on the Mesorch architecture, the paper introduces two baseline models aimed at solving IML tasks through mesoscopic representation. Extensive experiments across four datasets have demonstrated that our models surpass the current state-of-the-art in terms of performance, computational complexity, and robustness.

Zhuohang Jiang, Bingkui Tong, Xia Du et al.

The Privacy-sensitive Object Identification (POI) task allocates bounding boxes for privacy-sensitive objects in a scene. The key to POI is settling an object's privacy class (privacy-sensitive or non-privacy-sensitive). In contrast to conventional object classes which are determined by the visual appearance of an object, one object's privacy class is derived from the scene contexts and is subject to various implicit factors beyond its visual appearance. That is, visually similar objects may be totally opposite in their privacy classes. To explicitly derive the objects' privacy class from the scene contexts, in this paper, we interpret the POI task as a visual reasoning task aimed at the privacy of each object in the scene. Following this interpretation, we propose the PrivacyGuard framework for POI. PrivacyGuard contains three stages. i) Structuring: an unstructured image is first converted into a structured, heterogeneous scene graph that embeds rich scene contexts. ii) Data Augmentation: a contextual perturbation oversampling strategy is proposed to create slightly perturbed privacy-sensitive objects in a scene graph, thereby balancing the skewed distribution of privacy classes. iii) Hybrid Graph Generation & Reasoning: the balanced, heterogeneous scene graph is then transformed into a hybrid graph by endowing it with extra "node-node" and "edge-edge" homogeneous paths. These homogeneous paths allow direct message passing between nodes or edges, thereby accelerating reasoning and facilitating the capturing of subtle context changes. Based on this hybrid graph... **For the full abstract, see the original paper.**

Zhuohang Jiang, Bingkui Tong, Xia Du et al.

With the rise of social platforms, protecting privacy has become an important issue. Privacy object detection aims to accurately locate private objects in images. It is the foundation of safeguarding individuals' privacy rights and ensuring responsible data handling practices in the digital age. Since privacy of object is not shift-invariant, the essence of the privacy object detection task is inferring object privacy based on scene information. However, privacy object detection has long been studied as a subproblem of common object detection tasks. Therefore, existing methods suffer from serious deficiencies in accuracy, generalization, and interpretability. Moreover, creating large-scale privacy datasets is difficult due to legal constraints and existing privacy datasets lack label granularity. The granularity of existing privacy detection methods remains limited to the image level. To address the above two issues, we introduce two benchmark datasets for object-level privacy detection and propose SHAN, Scene Heterogeneous graph Attention Network, a model constructs a scene heterogeneous graph from an image and utilizes self-attention mechanisms for scene inference to obtain object privacy. Through experiments, we demonstrated that SHAN performs excellently in privacy object detection tasks, with all metrics surpassing those of the baseline model.