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Tampering Localization in Double Compressed Images by Investigating Noise Quantization

Tampering Localization in Double Compressed Images by Investigating Noise Quantization

Archana Vasant Mire, Sanjay B. Dhok, Naresh J. Mistry, Prakash D. Porey
Copyright: © 2020 |Pages: 18
ISBN13: 9781799830252|ISBN10: 179983025X|EISBN13: 9781799830269
DOI: 10.4018/978-1-7998-3025-2.ch024
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MLA

Mire, Archana Vasant, et al. "Tampering Localization in Double Compressed Images by Investigating Noise Quantization." Digital Forensics and Forensic Investigations: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2020, pp. 336-353. https://doi.org/10.4018/978-1-7998-3025-2.ch024

APA

Mire, A. V., Dhok, S. B., Mistry, N. J., & Porey, P. D. (2020). Tampering Localization in Double Compressed Images by Investigating Noise Quantization. In I. Management Association (Ed.), Digital Forensics and Forensic Investigations: Breakthroughs in Research and Practice (pp. 336-353). IGI Global. https://doi.org/10.4018/978-1-7998-3025-2.ch024

Chicago

Mire, Archana Vasant, et al. "Tampering Localization in Double Compressed Images by Investigating Noise Quantization." In Digital Forensics and Forensic Investigations: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 336-353. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-3025-2.ch024

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Abstract

Noise is uniformly distributed throughout an untampered image. Tampering operations destroy this uniformity and introduce inconsistency in the tampered region. Hence, noise discrepancy is often investigated in forensic analysis of uncompressed digital images. However, noise in compressed images has got very little attention from the forensic experts. The JPEG compression process itself introduces uniform quantization noise throughout an image, making this investigation difficult. In this paper, the authors have proposed a new noise compression discrepancy model, which blindly estimates this discrepancy in the compressed images. Considering the smaller tampered region, SVM classifier was trained using noise features of test sub-images and its nonaligned recompressed versions. Each of the test sub-images was further classified using this classifier. Experimental results show that in some cases, the proposed approach can achieve better performance compared with other JPEG artefact based techniques.

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