Near-duplicate detection for LCD screen acquired images using edge histogram descriptor

The expressive ability of digital visual media with the simplicity of their acquisition, processing, distribution, manipulation, and storage is such that they are utilized more to convey information over other sources of the information carriers. Formerly and conventionally, there has been credence in the authenticity of digital visual media. Still, with the availability of inexpensive and easy to use digital devices with high-resolution cameras coming in handy in mobile phones and the availability of low-cost and user-convenient editing tools, visual media forgery is ubiquitous. In general, forgery introduces certain artifacts in digital images. The recapturing process eliminates those artifacts and misleads the forensic system. The research work proposed in this paper presents a novel technique for the detection of near-duplicate images by examining the edge profile obtained by the edge histogram descriptor. The difference between the numbers of grouped directional edges present in singly captured and the near-duplicate image is used to build the feature vectors. Based on the training of those feature vectors, a model is generated using the SVM classifier. The proposed method is tested on three datasets of high-resolution and high-quality near-duplicate images, namely, NTU-ROSE, ICL, and Mturk. The evaluated results exemplify that the technique proposed is comparatively better than the state-of-the-art methods for near-duplicate detection. Features extracted from the image of vector length 91 allows an SVM classifier to achieve the precision of 100% and selectivity value above 97%. Furthermore, our results show that the proposed method achieves a performance rate that exceeds the overall accuracy of 99% for all three datasets.