Robust dense-field based copy-move forgery localization using generic radial harmonic Fourier moment invariants

As the importance of reliable multimedia content increases in today's society, image forensics is a growing field of research. The act of copying and pasting specific parts of an image, known as copy-move forgery, may be utilized for illegal or unethical purposes. Just as with other vision-related technologies, the accuracy of forensic analysis depends on having an appropriate image representation. Most existing feature extraction techniques do not accurately reflect the underlying image content leading to reduced performance. In this article, to detect the copy-move forgery attack, the Generic Radial Harmonic Fourier Moment (GRHFM) is proposed for reliable and distinctive image representation. The algorithm has the ability to effectively manipulate the distribution of zeros to emphasize certain image regions. Additionally, the relationships between complex exponentials and trigonometric functions are exploited to efficiently compute and easily implement the transform kernels. The efficacy of the algorithm is illustrated through experiments on dense-domain-based matching patterns. Experimental results on five benchmarking databases prove the effectiveness of the proposed approach compared with the state-of-the-art methods. According to the average scores, the proposed method demonstrates superior accuracy in overall localization performance. The F1 score, precision, and recall percentage values obtained are 92.5, 95.44, and 91.96, respectively. Robustness experiments on more challenging attacks are also conducted on FAU dataset. Results show that the proposed framework satisfies invariance to the various image variations, and thus an enhanced robustness compared to the previous methods. Moreover, the advantage of reasonable computational cost implies its potential use in real-world forensic applications.