An Images Regeneration Method for CG Anti-Forensics Based on Sensor Device Trace

Sensor device trace are the specific characteristics of natural images (NI), which are also the primary focus of many advanced forensic algorithms. However, most existing anti-forensic methods only consider the pixel-level differences between NI and computer-generated graphics (CG), while ignoring sensor device trace in NI. In this paper, we propose a novel anti-forensic method called SDTNet, which captures the trace of sensor device and integrates them with CG. Initially, the image content is suppressed by applying a high-pass filter, followed by the use of multiple convolution layers in an inverse manner. Subsequently, the sensor device trace is extracted using the Siamese network. Finally, this extracted trace is fused with CG at the feature level. The proposed method enables the generation of anti-forensic images that more accurately simulate the trace of NI device. Compared with the existing methods, the proposed method demonstrates superior performance in both visual quality and anti-forensic capabilities.