Let Images Speak More: An Efficient Method for Detecting Image Manipulation History

Digital image forensics aims to verify the authenticity of digital images, which has emerged as a prominent research area. To reveal the manipulation history of an image, the existing methods can only detect specific image operations or are based on a general forensic feature with high dimensions. Moreover, these methods perform well only when the operation chain length is no greater than 2. However, their detection accuracy drops significantly for images with longer operation chains that are more representative of real-world scenarios. To break these limitations, we proposed a novel forensics frequency Feature based on Histogram and Detail Map (FHDM<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(79D)</sup>), which can distinguish various operation chains containing different numbers of operations. Specifically, compared to the traces left by image manipulation in the spatial domain, we have discovered that they are more distinct in the frequency domain. This observation has prompted us to extract features from the frequency domain of images by analyzing their histograms and detail maps to capture the manipulation traces of the images. Notably, the proposed feature extracted in the frequency domain has almost 90% fewer dimensions than the commonly used general forensic features, such as SRM<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(714D)</sup>, which greatly reduces the computational complexity. Meanwhile, compared to deep learning-based methods, the experiments show that the proposed method achieves a detection accuracy of over 95% for image operations across multiple datasets, while other deep learning-based methods do not exceed 90% accuracy. Extensive experimental results show that the proposed method is more versatile and effective, showing good performance in complex operation chain detection and local forgery detection. The code is available at https://github.com/CherishL-J/Op-detection.