Exposing image splicing with inconsistent sensor noise levels
- PDF / 3,259,055 Bytes
- 16 Pages / 439.37 x 666.142 pts Page_size
- 0 Downloads / 204 Views
Exposing image splicing with inconsistent sensor noise levels Hui Zeng 1
1
& Anjie Peng & Xiaodan Lin
2
Received: 30 June 2019 / Revised: 31 May 2020 / Accepted: 29 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Splicing is a commonly used image tampering operation, where a part of one image is pasted into another image. The forged image can have completely different semantic from the original one and may mislead people in some serious occasions. To rebuild the credibility of the images, extensive forensic methods aiming to locate the spliced areas have been proposed in recent years. Among these methods, the noise based ones, which utilize the fact that images from different sources tend to have various noise levels, have drawn much attention due to their convenience to implement. However, most of the existing noise based methods are under the assumption that a synthetic additional white Gaussian noise (AWGN) is involved during the splicing. This maybe not the case in practice. In this study, we utilize the difference of the intrinsic sensor noise of the source images to expose the potential image splicing. In practice, the sensor noise level difference is common between images captured with different ISO settings. Through analyzing the characteristics of the sensor noise, a weighted noise level is proposed for reducing influences from image content thus can better localizing the splicing region. Specifically, the noise level of a questioned image is first estimated locally with principal component analysis (PCA)-based algorithm. Then, the estimated noise levels are weighted before clustering with k-means. The experimental results demonstrate the superiority of the proposed method over several state-of-the-art methods, not only for splicing localization purpose, but also for splicing detection purpose. Keywords Image splicing . Additional white Gaussian noise (AWGN) . Sensor noise level . ISO settings . Principal component analysis (PCA) . K-means
1 Introduction Thanks to the rapid development of user friendly and powerful image editing software, such as Meitu and Photoshop, even an amateur user can modify images with little visual artifacts. As a
* Anjie Peng [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
consequence, the credibility of image contents as evidences are increasingly challenged. To rebuild the credibility of the images, extensive forensic methods aiming to reveal the image tampering have be proposed both in academic [1, 20] and in industry [18]. There are two most commonly used image-tampering techniques that can significantly change the content of the original image: copy-move and image splicing. In the copy-move tampering, a region of an image is replicated and pasted on the same image, which usually results in duplicated regions in the tampered image [1, 9, 23]. Another tampering technique is image splicing, where a forged image is generated by splicing regions from
Data Loading...
