Abstract: With the widespread use of digital images in social media, they have become the core carrier of information dissemination. The rise of powerful and easy-to-use image editing software and generative artificial intelligence technology has lowered the threshold for creation while also providing a more covert way for malicious image tampering, leading to the accelerated spread of false information. Tampering will leave specific tampering features in the image, which constitutes the core basis of image tampering detection technology. In the face of increasingly complex and diverse tampering methods, most of the existing reviews focus on a single technical route, and lack systematic comparison and integrated analysis of image tampering detection techniques. To this end, this paper constructs a three-dimensional classification system of "feature traceability-extraction method-detection task", summarizes the image tamper detection technology into two categories based on manual features and deep learning features, and carries out the following work: First, the system reconstructs the classification framework of image tamper detection technology based on manual features, and integrates the scattered manual features in traditional research into three categories: camera system features, pixel-level features and format-related features. The physical mechanism and improvement effect of the performance optimization strategies of 14 typical image tamper detection technologies are deeply analyzed, and the shortcomings of the existing review in the systematic analysis of image tamper detection technology based on manual features are thoroughly analyzed. Second, the image tamper detection technology based on deep learning is sorted out in architecture, and the generative image tamper detection technology is analyzed. Third, the composition, characteristics and limitations of the existing tampered image datasets are summarized and commented on, and the selection of datasets is provided with a selectable basis. Finally, this paper summarizes and looks forward to the future research direction and development trend of this field, and points out some key scientific issues that need to be solved urgently, in order to provide reference for subsequent research.

摘要： 随着数字图像在社交媒体中的广泛应用，其已成为信息传播的核心载体。功能强大且易于使用的图像编辑软件与生成式人工智能技术的兴起，在降低创作门槛的同时，也为图像恶意篡改提供了更隐蔽的途径，导致虚假信息加速扩散。篡改行为会在图像中留下具有特异性的篡改特征，构成了图像篡改检测技术的核心依据。面对日益复杂且多样化的篡改手段，现有综述多聚焦于单一技术路线，缺乏对图像篡改检测技术系统性对比与整合分析。为此，本文构建“特征溯源—提取方式—检测任务”三维分类体系，基于篡改特征将图像篡改检测技术归纳为基于手工特征和基于深度学习特征两大类，并进行以下工作：其一，系统重构基于手工特征的图像篡改检测技术分类框架，将传统研究中分散的手工特征整合为相机系统特征、像素级特征与格式相关特征三大类，深入剖析14种典型图像篡改检测技术的性能优化策略物理机理与改进效果，弥补现有综述在基于手工特征的图像篡改检测技术系统性分析方面的不足；其二，架构化梳理基于深度学习特征的图像篡改检测技术，并针对生成式图像篡改检测技术进行着重分析；其三，对现有篡改图像数据集的构成、特点及其局限性进行归纳与评述，为数据集选择提供可选择的依据；最后，总结展望该领域在未来的研究方向和发展趋势，指出若干亟待解决的关键科学问题，以期为后续研究提供参考与借鉴。