Mitigating Adaptive PRNU Denoising in Camera Model Identification: An Anti–Counter Forensic Approach

Adaptive PRNU Denoising (APD) is one of the strongest forms of state-of-the-art counter-forensic attacks, that has largely succeeded to prevent Photo Response Non-Uniformity (PRNU) based source identification of digital images. A major way to thwart such attack would be finding a successful mapping of a counter-forensically modified image, back to its source. In other words, such attack will be rendered obsolete if accurate source identification can be made possible with the counter-forensically modified images. For the first time, we perform a feature based identification of image sources, working directly with counter-forensically modified images, without trying to recover their authentic versions. We quantize the effects of APD on forensic source identification techniques, and explore how image texture helps in source identification of APD counter-forensic images. We propose Local Binary Pattern (LBP) extraction on APD image texture layer, the key component of our work, enabling successful counter-forensic image source attribution. Our experimental results prove that the proposed methodology is highly effective to mitigate the threats posed by APD, one of the strongest counter-forensic attacks on camera model identification today.