Detectability-quality trade-off in JPEG counter-forensics
Removing JPEG quantization footprints from an image inevitably introduces artifacts and traces in the spatial domain. Recently, several robust methods have been proposed to detect footprints of counter-forensics and recover the image's compression history. In this paper we investigate the limitations of these detectors, by proposing an improved counter-forensic attack which adds a postprocessing denoising step besides dithering. We consider both a general-purpose denoising algorithm and one targeted to JPEG images. In the latter case, we show that this approach can successfully reduce the accuracy of detectors in the literature to that of a random decision. As a second contribution, we study the trade-off between the detectability of counter-forensics and quality of the tampered image, and show that the loss of quality is not sufficient for the analyst to use available no-reference quality assessment tools as an indicator of an attack.