Abstract
Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.
Highlights
Digital images are being edited deliberately or involuntarily to make them more informative or to hide some content in the image
A novel CMFD using texture features obtained from gray level co-occurrence matrix (GLCM) on differential excitation component (DEC) is proposed and significance of texture features obtained from GLCM and Discrete Wavelet Transform (DWT)-GLCM is presented
The following parameters are calculated to evaluate the performance of the classifier: True Positive (TP) – Forged Images predicted as Forged True Negative (TN) – Original Images predicted as Original False Positive (FP) – Original Images predicted as Forged False Negative (FN) – Forged Images predicted as Original
Summary
Digital images are being edited deliberately or involuntarily to make them more informative or to hide some content in the image. The vast growth of commercial and open source digital photo editing tools leads to the increase of tampered images in day-to-day life. The trustworthiness of digital image plays a major role in many applications, viz., criminal examination, journalism, forensic analysis and surveillance systems (Mahdian & Saic, 2010). A beginner in digital forensics area can refer to its various applications in (Li, 2013). Digital Image Forgery (DIF) detection is plausible in two approaches (Hashmi & Keskar, 2015), viz., Active and Passive. Active approach involves pre-processing of a genuine image by embedding an identifier before it is used. Watermarking and signature embedding technologies are active approaches useful for detection and localization of image forgery but pre-
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