Effect of JPEG compression on Sensor-based Image Forensics

2021 ◽  
Author(s):  
Sujoy Chakraborty
Keyword(s):  
Image Forensics ◽  
Jpeg Compression

Image Forensics Using Generalised Benford’s Law for Improving Image Authentication Detection Rates in Semi-Fragile Watermarking

2012 ◽  
pp. 36-52
Author(s):  
Xi Zhao ◽  
Anthony T.S. Ho ◽  
Yun Q. Shi
Keyword(s):  
Image Authentication ◽  
Image Forensics ◽  
Jpeg Compression ◽  
Fragile Watermarking ◽  
Benford’S Law ◽  
Quality Factors ◽  
Detection Rates ◽  
Benford's Law ◽  
Improving Accuracy ◽  
The Relationship

In the past few years, semi-fragile watermarking has become increasingly important to verify the content of images and localise the tampered areas, while tolerating some non-malicious manipulations. In the literature, the majority of semi-fragile algorithms have applied a predetermined threshold to tolerate errors caused by JPEG compression. However, this predetermined threshold is typically fixed and cannot be easily adapted to different amounts of errors caused by unknown JPEG compression at different quality factors (QFs). In this paper, the authors analyse the relationship between QF and threshold, and propose the use of generalised Benford’s Law as an image forensics technique for semi-fragile watermarking. The results show an overall average QF correct detection rate of approximately 99%, when 5%, 20% and 30% of the pixels are subjected to image content tampering and compression using different QFs (ranging from 95 to 65). In addition, the authors applied different image enhancement techniques to these test images. The proposed image forensics method can adaptively adjust the threshold for images based on the estimated QF, improving accuracy rates in authenticating and localising the tampered regions for semi-fragile watermarking.

Image Forensics Using Generalised Benford's Law for Improving Image Authentication Detection Rates in Semi-Fragile Watermarking

2010 ◽  
Vol 2 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Xi Zhao ◽  
Anthony T. S. Ho ◽  
Yun Q. Shi
Keyword(s):  
Image Authentication ◽  
Image Forensics ◽  
Jpeg Compression ◽  
Fragile Watermarking ◽  
Benford’S Law ◽  
Quality Factors ◽  
Detection Rates ◽  
Benford's Law ◽  
Improving Accuracy ◽  
The Relationship

In the past few years, semi-fragile watermarking has become increasingly important to verify the content of images and localise the tampered areas, while tolerating some non-malicious manipulations. In the literature, the majority of semi-fragile algorithms have applied a predetermined threshold to tolerate errors caused by JPEG compression. However, this predetermined threshold is typically fixed and cannot be easily adapted to different amounts of errors caused by unknown JPEG compression at different quality factors (QFs). In this paper, the authors analyse the relationship between QF and threshold, and propose the use of generalised Benford’s Law as an image forensics technique for semi-fragile watermarking. The results show an overall average QF correct detection rate of approximately 99%, when 5%, 20% and 30% of the pixels are subjected to image content tampering and compression using different QFs (ranging from 95 to 65). In addition, the authors applied different image enhancement techniques to these test images. The proposed image forensics method can adaptively adjust the threshold for images based on the estimated QF, improving accuracy rates in authenticating and localising the tampered regions for semi-fragile watermarking.

SAE based unified double JPEG compression detection system for Web image forensics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhifeng Wang ◽  
Chi Zuo ◽  
Chunyan Zeng
Keyword(s):  
Detection System ◽  
Image Forensics ◽  
Difficult Problem ◽  
Jpeg Compression ◽  
Quality Factors ◽  
Unified Framework ◽  
Intrinsic Structure ◽  
Content Type ◽  
Double Jpeg Compression ◽  
Web Images

Purpose Recently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression. Design/methodology/approach First, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression. Findings Experimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches. Practical implications To verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions. Originality/value This paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.

scholarly journals Analyzing Benford’s Law’s Powerful Applications in Image Forensics

2021 ◽  
Vol 11 (23) ◽  
pp. 11482
Author(s):  
Diana Crișan ◽  
Alexandru Irimia ◽  
Dan Gota ◽  
Liviu Miclea ◽  
Adela Puscasiu ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Quality Factor ◽  
Digital Images ◽  
Image Forensics ◽  
Jpeg Compression ◽  
Point Of View ◽  
Natural Numbers ◽  
Quality Factors ◽  
Jpeg Image ◽  
Benford Law

The Newcomb–Benford law states that in a set of natural numbers, the leading digit has a probability distribution that decays logarithmically. One of its major applications is the JPEG compression of images, a field of great interest for domains such as image forensics. In this article, we study JPEG compression from the point of view of Benford’s law. The article focuses on ways to detect fraudulent images and JPEG quality factors. Moreover, using the image’s luminance channel and JPEG coefficients, we describe a technique for determining the quality factor with which a JPEG image is compressed. The algorithm’s results are described in considerably more depth in the article’s final sections. Furthermore, the proposed idea is applicable to any procedure that involves the analysis of digital images and in which it is strongly suggested that the image authenticity be verified prior to beginning the analyzing process.

A robust JPEG compression detector for image forensics

2020 ◽  
Vol 89 ◽  
pp. 116008
Author(s):  
Chothmal Kumawat ◽  
Vinod Pankajakshan
Keyword(s):  
Image Forensics ◽  
Jpeg Compression

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

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