Double JPEG Compression Detection Based on Noise-Free DCT Coefficients Mixture Histogram Model

Zhu, Nan; Shen, Junge; Niu, Xiaotong

doi:10.3390/sym11091119

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…The parameter estimation was further improved in [37] by exploiting the smoothness of the model likelihood function. Another improvement was proposed in [38] where quantization noise is eliminated before solving the mixture histogram model. More recently, Xue et al developed in [39] a mixture model based on normalized gray level co-occurrence matrix to localize tampering in JPEG images.…”

Section: A Prior Artmentioning

confidence: 99%

A Statistical Modeling Framework for DCT Coefficients of Tampered JPEG Images and Forgery Localization

Retraint

2022

IEEE Access

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Various manipulations on JPEG images introduce single and multiple compression artifacts for forged and unmodified areas respectively. Based on the statistical analysis of JPEG compression cycle and on the finite mixture paradigm, we propose in this paper a modeling framework for AC DCT coefficients of such tampered JPEG images. Its accuracy is numerically assessed using the Kullback-Leibler divergence on the basis of a tampered JPEG image dataset built from six well-known uncompressed color image databases. To illustrate the framework utility, an application in image forgery localization is proposed. By formulating the localization as a clustering problem, we use the plug-in Bayes rule combined with a simple EM algorithm to distinguish between forged and unmodified areas. Numerous experiments show that, when the quality factor of final JPEG compression is high enough, the proposed modeling framework yields higher localization performances in terms of F 1 -score than prior art regardless of divers local manipulations.INDEX TERMS DCT coefficients analysis, EM algorithm, forgery localization, multiple JPEG compression, statistical image models, tampered JPEG images

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Section: A Prior Artmentioning

confidence: 99%

A Statistical Modeling Framework for DCT Coefficients of Tampered JPEG Images and Forgery Localization

Retraint

2022

IEEE Access

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“…Li et al [5] developed a passive detection algorithm to detect tampered JPEG images using anomalous block artifact grids. Zhu et al [6] proposed an improved double JPEG compression detection algorithm based on noise-free DCT coefficient mixture histogram model. Bianchi et al [7] obtained a single feature from DCT coefficient statistics to detect NA-JPEG compression.…”

Section: Related Workmentioning

confidence: 99%

“…Image compression features include block artifacts [4] and Block Artifact Grids (BAG) [5] generated by JPEG compression for checking image inconsistency. Then, the features of aligned double JPEG compression [6] and Non-Aligned double JPEG (NA-JPEG) compression [7] are introduced to detect image tampering. For image local noise features, the tamper traces are revealed based on the local noise variance models of wavelet filtering [8] and the kurtosis characteristics of the frequency sub-band coefficients [9] in the forged images.…”

Section: Introductionmentioning

confidence: 99%

Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

Wei

Dong

et al. 2019

Symmetry

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Due to the wide availability of the tools used to produce manipulated images, a large number of digital images have been tampered with in various media, such as newspapers and social networks, which makes the detection of tampered images particularly important. Therefore, an image manipulation detection algorithm leveraged by the Faster Region-based Convolutional Neural Network (Faster R-CNN) model combined with edge detection was proposed in this paper. In our algorithm, first, original tampered images and their detected edges were sent into symmetrical ResNet101 networks to extract tampering features. Then, these features were put into the Region of Interest (RoI) pooling layer. Instead of the RoI max pooling approach, the bilinear interpolation method was adopted to obtain the RoI region. After the RoI features of original input images and edge feature images were sent into bilinear pooling layer for feature fusion, tampering classification was performed in fully connection layer. Finally, Region Proposal Network (RPN) was used to locate forgery regions. Experimental results on three different image manipulation datasets show that our proposed algorithm can detect tampered images more effectively than other existing image manipulation detection algorithms.

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“…Since human vision is not very sensitive to some small changes in the image, lossy compression can be achieved, which can reach higher compression ratio than lossless compression, thereby improving the storage and transmission efficiency of images. Common lossy compression such as: DCT transform [5], wavelet transform, compressed sensing [6] and so on. In addition, fractal coding is also an image compression method [7].…”

Section: Introductionmentioning

confidence: 99%

A chaos-based image compression and encryption scheme using fractal coding and adaptive-thresholding sparsification

et al. 2022

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With the wide application of digital images in the network, the problems of information security and transmission efficiency need to be solved urgently. Fractal encoding as an efﬁcient compression method, its security is not guaranteed. To solve these problems, this paper proposes a new image compression and encryption scheme based on chaotic map and frequency division joint compression coding, which uses the combination of fractal encoding and adaptive-thresholding sparsiﬁcation to compress images. Arnold scrambling and chaos-based plane scrambling diffusion are embedded in two compression processes respectively. In addition, an efficient encryption algorithm is designed based on the coupling-enhanced chaotic map, which encrypt the compressed data as a whole through simultaneous scrambling of parent blocks and sub-blocks and zigzag round-trip diffusion. The simulation results demonstrate that the proposed image compression and encryption scheme is suitable for gray and color images, and can achieve higher compression ratio and improve the efficiency of fractal compression. The designed encryption algorithm is reliable, and more efficient than several advanced image encryption schemes.

show abstract

Double JPEG Compression Detection Based on Noise-Free DCT Coefficients Mixture Histogram Model

Cited by 6 publications

References 26 publications

A Statistical Modeling Framework for DCT Coefficients of Tampered JPEG Images and Forgery Localization

A Statistical Modeling Framework for DCT Coefficients of Tampered JPEG Images and Forgery Localization

Developing an Image Manipulation Detection Algorithm Based on Edge Detection and Faster R-CNN

A chaos-based image compression and encryption scheme using fractal coding and adaptive-thresholding sparsification

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