An Improved Double Compression Detection Method for JPEG Image Forensics

Thing, Vrizlynn L. L.; Yu, Chunshui; Cheh, Carmen

doi:10.1109/ism.2012.61

Cited by 21 publications

(7 citation statements)

References 7 publications

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“…Therefore, the unmodified region will exhibit double compression artefacts but the forged regions would not have any artefacts. Thing et al (2012) introduced an improved double compression detection method for JPEG image forensics. The focus of their research was on a detection method that is more suited for large scale image authenticity verification.…”

Section: Jpeg Re-compressionmentioning

confidence: 99%

“…As mentioned earlier in Section 2, JPEG images that have splicing would inherently undergo a double JPEG compression thus exhibiting a DQ effect (Bianchi and Piva, 2011;Thing et al, 2012). So, by capturing the underlying statistics of the DCT coefficients, such tampering can be determined.…”

Section: Discrete Cosine Transformmentioning

confidence: 99%

“…Some well known methods includes analysing the discrete cosine transform (DCT) coefficients (Fridrich et al, 2003;Popescu and Farid, 2004) or the discrete wavelet transforms (DWT) (Li et al, 2007;Zhang et al, 2008) of the images to identify copy-move tampering. Some of the proposed work also studied the use of double compression effects in the spatial and DCT domains to identify tampered regions (Lin and Wu, 2011;Thing et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection

Goh

Thing

2015

IJESDF

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The detection of the presence of tampered images is of significant importance in digital forensics. The problem with image tampering detection is the vast number of features that are currently available in the literature. It is very challenging to determine the best features to correctly characterise these images. This paper proposes a hybrid evolutionary framework to perform a quantitative study to evaluate all features in image tampering for the best feature set. Upon feature evaluation and selection, the classification mechanism must be optimised for good performance. Therefore, in addition to being able to determine an optimal set of features for a classifier, the hybrid framework is capable of determining the optimal multiple classifier ensembles while achieving the best classification performance in terms of low complexity and high accuracy for image tampering detection. Using a training dataset of only 5% of the dataset, we were able to obtain accuracies of 90.18% on a CASIA 1 dataset with 1,457 test images, 96.21% on a CASIA 2 dataset with 10,200 and 94.64% on a combined CASIA 1 and 2 dataset with 11,657 testing images. The experiment result shows that our image tampering detection can support large-scale digital image evidence authenticity verification with consistent good accuracy.Reference to this paper should be made as follows: Goh, J. and Thing, V.L.L. (2015) 'A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection', Int.

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Section: Jpeg Re-compressionmentioning

confidence: 99%

Section: Discrete Cosine Transformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection

Goh

Thing

2015

IJESDF

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“…The author used Transition Probability Matrix to improve [8] and a SVM classifier is used to perform the double JPEG detection. Zhang et al in their paper in [10] [11] (2012) tried to improve the accuracy of JPEG image tampering detection by considering the characteristics of the random distribution of high value bins in the DCT histograms. Author used CASIA authentic and tampered image data set of 9501 JPEG images.…”

Section: Introductionmentioning

confidence: 99%

Forensics, Anti-forensics and Counter Anti-forensics for JPEG Compressed Images

2016

IJCCIE

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Abstract---With the advancement in information technology and image processing software the manipulation of the images has increased considerably from past few decades. Retouching or tampering of the images has been so prevalent that nowadays one can hardly believe their originality. Image authenticity is done using forgery detection techniques. These methods are broadly classified as active and passive. Aim of this paper is to give state of art for a passive detection technique i.e. JPEG double compression. Its forensics, anti-forensics and counter anti-forensics is presented with a review of technologies that exist so far.

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“…Thuong Le-Tien, Hanh Phan-Xuan, Thuy Nguyen-Chinh, and Thien Do-Tieu Image Forgery Detection: A Low Computational-Cost and Effective Data-Driven Model both of aligned and non-aligned double JPEG compression cases. L. Thing et al[11] introduced a new periodic detection method of double quantization. Explicitly, they exploited properties of the Gaussian distribution of most significant bins in the DCT histograms of nature images.…”

mentioning

confidence: 99%

Image Forgery Detection: A Low Computational-Cost and Effective Data-Driven Model

Le-Tien¹,

Phan-Xuan²,

Nguyen-Chinh³

et al. 2019

IJMLC

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Nowadays, Image Forgery Detection contributes an indispensable role in digital forensics, while there are increasingly more sophisticated forgery methods. In overall, almost conventional methods just focus on identifying specific features in tampered images, therefore, such methods cannot cover whole possible cases in reality. Recently, some data-driven proposals have been exploited to handle these barriers and attained prominent results. However, almost these ones are hungry to data because of the complication in deep architectures, which requires a large amount of data and an energetic implementation hardware. In this paper, we propose a low computational-cost and effective data-driven model as a modified deep learning-based model to solve the existing problems above. The process of approach is overviewed as follows: Firstly, the Daubechies Wavelet transform is utilized to extract features of size 450, representing YCrCb patches inside the image. Then, a neural network is used to classify forged patches. However, when conducting a discrimination analysis, we found that the luminance channel (Y) does not play an essential role in the forgery detection, whereas, it is better by using two chrominance channels (Cr and Cb). The idea is stated by removing these luminance features, then the feature vector dimension changes to as two-thirds as its origin, which reduces efficiently the computational cost in both of training and testing processes. The experimental results reveal that our proposed method reaches a high detection accuracy of 97.11%, even the model suffers in some difficult circumstances (e.g., narrowness, and lack of positive training samples). As a result, the proposed model is effective to address the mentioned challenges.

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An Improved Double Compression Detection Method for JPEG Image Forensics

Cited by 21 publications

References 7 publications

A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection

A hybrid evolutionary algorithm for feature and ensemble selection in image tampering detection

Forensics, Anti-forensics and Counter Anti-forensics for JPEG Compressed Images

Image Forgery Detection: A Low Computational-Cost and Effective Data-Driven Model

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