A context-adaptive SPN predictor for trustworthy source camera identification
EURASIP Journal on Image and Video Processing
Sensor pattern noise (SPN) has been recognized as a reliable device fingerprint for camera source identification (CSI) and image origin verification. However, the SPN extracted from a single image can be contaminated largely by image content details from scene because, for example, an image edge can be much stronger than SPN and hard to be separated. So, the identification performance is heavily dependent upon the purity of the estimated SPN. In this paper, we propose an effective SPN predictor
... ctive SPN predictor based on eight-neighbor context-adaptive interpolation algorithm to suppress the effect of image scene and propose a source camera identification method with it to enhance the receiver operating characteristic (ROC) performance of CSI. Experimental results on different image databases and on different sizes of images show that our proposed method has the best ROC performance among all of the existing CSI schemes, as well as the best performance in resisting mild JPEG compression, especially when the false-positive rate is held low. Because trustworthy CSI must often be performed at low false-positive rates, these results demonstrate that our proposed technique is better suited for use in real-world scenarios than existing techniques. However, our proposed method needs many such as not less than 100 original images to create camera fingerprint; the advantage of the proposed method decreases when the camera fingerprint is created with less original images.