Exploiting PRNU and Linear Patterns in Forensic Camera Attribution under Complex Lens Distortion Correction

More complex and ever more common lens distortion correction post-processing is seriously hampering state-of-the-art camera attribution techniques. In this paper, we show that the two main existing techniques, namely PRNU (Photo Response Non Uniformity)-based and linear-pattern-based, can be successfully combined to improve performance. Moreover, we introduce a novel method that is able to correctly invert adaptive distortion correction transformations by successively maximizing the peak-to-correlation energy (PCE) and the linear-pattern energy for much more reliable camera attribution. A novel validation procedure to quickly discard mismatched test images is also proposed. Finally, we show how great reductions in running time can be achieved by using a GPU for interpolation, resampling, and PCE computation. The code is available at https://github.com/AMontiB/PSLR.