Detection of copy–move image forgery using histogram of orientated gradients

The increasing popularity of digital media and media editing software has led to widespread tampering of multimedia files for malicious purposes. The most common form of tampering associated with digital images is copy–move forgery, in which a portion of an image is duplicated and substituted in a different location. Thus, law enforcement and forensics experts require reliable and efficient means of detecting copy–move forgery. This paper proposes a blind forensics approach to the detection of copy–move forgery. The input image is segmented into overlapping blocks, whereupon a histogram of orientated gradients is applied to each block. Statistical features are extracted and reduced to facilitate the measurement of similarity. Finally, feature vectors are lexicographically sorted, and duplicated image blocks are detected by identifying similar block pairs after post-processing. Experiment results demonstrate that the proposed method is able to detect multiple examples of copy–move forgery and precisely locate the duplicated regions, even when dealing with images distorted by translation involving small rotations, blurring, adjustment of brightness, and color reduction. We are currently working to improve detection in regions with rotation and scaling adjustment over large areas.