Efficient Privacy-Preserving Forensic Method for Camera Model Identification

To address the camera origin identification problem of inquiry images, many forensic methods have been proposed. However, the heavy computational overhead and the potential threat of privacy leakage for inquiry images make many existing forensic methods less applicable. Only a few research works have proposed secure forensic methods to address the aforementioned issues; however, they did not give a detailed analysis for the statistical performance. In this paper, we propose an efficient privacy-preserving forensic method with analytical statistical performance to solve the camera model identification problem efficiently and securely. To preserve the privacy of inquiry images, we propose a hybrid privacy-preserving scheme consisting of two operations: Position Scrambling Encryption to preserve the privacy of image content and Noise Linear-Mapping Processing to preserve the privacy of camera model identity for inquiry images. In the encrypted domain where the proposed privacy-preserving scheme is employed, we first propose a novel statistical noise model, which can accurately characterize an encrypted JPEG inquiry image. Then, a noise model-based detector is designed to identify different camera models. Experimental results verify the feasibility of our proposed method from both privacy-preserving and forensic effectiveness and report that our method outperforms the state-of-the-art secure forensic methods, especially when sample images used to estimate camera fingerprints are insufficient, such as only 2 available images.