面向多畸变稳健性的图像归因算法

Image Attribution Algorithm with Multi-Distortion Robustness

随着多媒体编辑软件和生成式神经网络的发展,数字图像的可信度正在不断削弱。作为一种新兴的溯源式取证技术,图像归因回溯需分析图像的可信源和可视化图像的编辑性改变,因而能够有效对抗恶意篡改并辅助群体和个人对图像信息形成正确判断。但是目前的图像归因方法对网络空间中常见的几何变形和信号压缩表现不够稳定,特别是对于图像同时包含多种畸变的情况。为此,文章提出一种多畸变稳健的图像归因方法,该方法基于一种正交且协变的图像局部表征策略,具有对多种几何变换和信号损失的稳健性,同时设计了面向稀疏域和稠密域表征任务的两种快速计算方案。由此形成的图像归因方法能够有效回溯可信数据库中的近重复图像源,矫正待分析图像的几何姿态,并可视化潜在的图像篡改区域。该方法对网络空间中的多种良性变换具有稳健性,同时保持对恶性内容篡改的敏感性。仿真结果表明,该方法具有更优的篡改检测稳健性和综合检测精度,同时具有更优的特征紧凑性和实现成本。

With the development of multimedia editing software and generative neural networks,the reliability of digital images is being continuously eroded.As an emerging forensic technique for provenance analysis,image attribution retraces the trustworthy source of the image under analysis and visualizes the editorial changes in such image.Thus,it can effectively combat malicious manipulation,assisting users to form correct judgments on image information.However,current image attribution methods are not sufficiently robust to the geometric transformations or signal corruptions in modern cyberspace,especially for images that contain multiple distortions.For this gap,an image attribution method with multi-distortion robustness was proposed.The method was based on an orthogonal and covariant image local representation strategy with robustness to multiple geometric transformations or signal corruptions.Two fast implementations were designed for sparse and dense representation tasks,respectively.The resulting image attribution method was able to efficiently retrace near-duplicate source in a trusted database,correct the geometric pose,and visualize potential tampering regions.In such process,the proposed method was robust to various benign transformations while maintaining sensitivity to subtle content manipulation.Simulation results show that the proposed image attribution method exhibits better forgery detection robustness and overall accuracy,as well as better feature compactness and implementation cost.