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Universal Wavelet Relative Distortion: A New Counter–Forensic Attack on Photo Response Non-Uniformity Based Source Camera Identification

  • Venkata Udaya Sameer
  • Ruchira Naskar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11125)

Abstract

Photo Response Non–Uniformity (PRNU) is one of the most effective fingerprints used to detect the source camera of an image. Image Anonymization on the other hand, is a task of fooling the source camera identification, in order to protect the user’s anonymity in sensitive situations involving whistleblowers, social activists etc. To protect the privacy of users especially over the web, image anonymization is of huge importance. Counter–Forensic attacks on source camera identification try to make an image anonymous by nullifying the detection techniques. For almost every counter–forensic source camera identification attack, anti–counter attacks are being designed and hence there is a need to either strengthen the previous counter–forensic attacks or design a new attack altogether. In this work, we propose a new counter–forensic attack to source camera identification, using the Universal Wavelet Relative Distortion function designed for steganography. The main principle behind Universal Wavelet Relative Distortion is to embed changes in an image in regions such as textures or noisy parts which are crucial to source camera identification. We show through our experiments, when a random bit–string is inserted recursively in an image, the correlation strength of the noise residual based source camera identification gets significantly weak and such methods fail to map the source camera of the image under question. In the proposed method, the visual quality of the modified image is not changed, which makes our method a strong solution to image anonymization.

Keywords

Cybercrime Counter forensics Digital forensics Fingerprint PCE PSNR SSIM Steganography Source camera identification 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of TechnologyRourkelaIndia

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