Abstract
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 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.
Highlights
Digital images are easy to modify and edit via image-editing software
In order to reduce the impact of scene details while preserving sensor pattern noise (SPN) at the same time, an edge-adaptive SPN predictor based on a four-neighbor context-adaptive interpolation (PCAI4) [13] was proposed and has been proved to have improvement on camera source identification (CSI) performance via extensive experiments
4 Conclusion In this paper, we propose a source camera identification scheme based on an eight-neighbor context-adaptive SPN predictor to enhance the receiver operating characteristic (ROC) performance of CSI
Summary
Digital images are easy to modify and edit via image-editing software. Image content becomes unbelievable. The work in [3] first proposed using the imaging sensor pattern noise (SPN) to trace back the imaging device and solve the camera source identification (CSI) problem. They extracted SPN from wavelet high-frequency coefficients using the wavelet-based denoising filter [4]. A maximum likelihood method is there have been some prior studies dedicated to improving the performance of CSI based on SPN in recent years, an effective method to eliminate the contamination of the image scene details is still lacking. In order to reduce the impact of scene details while preserving SPN at the same time, an edge-adaptive SPN predictor based on a four-neighbor context-adaptive interpolation (PCAI4) [13] was proposed and has been proved to have improvement on CSI performance via extensive experiments. The experimental results on different image databases show that our proposed method can achieve the best ROC performance among all of the existing CSI schemes on different sizes of images and has the best performance in resisting mild JPEG compression
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
