An improved PRNU noise extraction model for highly compressed image blocks with low resolutions

Detecting sources in digital images and videos is crucial to multimedia forensics research. The inherent physical properties of imaging sensors result in the presence of Photo Response Non-Uniformity Noise (PRNU) within the captured multimedia content. This particular noise, often called the “fingerprint,” is a unique and stable feature for identifying the source camera. However, the compression of images by platform codecs on social network media introduces varying degrees of quantization noise, making to address this issue, a noise extractor based on variance stabilizing transform and adaptive block clustering principal component analysis (PCA) is proposed, along with an enhanced processing model that incorporates cyclic residual recycling. Firstly, the GAT and adaptive block clustering PCA filtering are applied to extract noises from the images. The obtained noises are then subjected to zero-mean and diagonal artifact elimination processing. Next, the real and imaginary parts of the noise spectrum are individually subjected to real-time iterative least squares smoothing based on half-quadratic optimization. Due to the presence of PRNU information in the residual between the pre-smoothed and post-smoothed signals, additional cyclic smoothing is applied to refine the signal further. Finally, the smoothed signals are accumulated to obtain the enhanced PRNU noise. Experimental comparisons conducted on the public dataset Dresden demonstrate that the proposed model significantly outperforms existing methods in terms of source camera identification for low-resolution and strong JPEG compression images.