Digital image integrity – a survey of protection and verification techniques

Korus, Paweł

doi:10.1016/j.dsp.2017.08.009

Cited by 113 publications

(61 citation statements)

References 95 publications

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“…Image forgery detection approaches are divided into active and passive (or blind) categories. Active approaches use detection of embedded watermarks or signatures to ensure the authenticity of images [18][19][20][21][22][23]. Such approaches are limited, because it is difficult to maintain prior information of such pre-embedded watermarks, signatures and secret keys [20].…”

Section: Related Workmentioning

confidence: 99%

Edge–texture feature-based image forgery detection with cross-dataset evaluation

Asghar

Sun

Rosin

et al. 2019

Machine Vision and Applications

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A digital image is a rich medium of information. The development of userfriendly image editing tools has given rise to the need for image forensics. The existing methods for the investigation of the authenticity of an image perform well on a limited set of images or certain datasets but do not generalize well across different datasets. The challenge of image forensics is to detect the traces of tampering which distorts the texture patterns. A method for image forensics is proposed, which employs Discriminative robust local binary patterns (DRLBP) for encoding tampering traces and a support vector machine (SVM) for decision making. In addition, to validate the generalization of the proposed method, a new dataset is developed that consists of historic images, which have been tampered with by professionals. Extensive experiments were conducted using the developed dataset as well as the public domain benchmark datasets; the results demonstrate the robustness and effectiveness of the proposed method for tamper detection and validate its cross-dataset generalization. Based on the experimental results, directions are suggested that can improve dataset collection as well as algorithm evaluation protocols. More broadly, discussion in the community is stimulated regarding the very important, but largely neglected, issue of the capability of image forgery detection algorithms to generalize to new test data.

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Section: Related Workmentioning

confidence: 99%

Edge–texture feature-based image forgery detection with cross-dataset evaluation

Asghar

Sun

Rosin

et al. 2019

Machine Vision and Applications

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“…It is important to identify fake information in order to avoid potential security issues. Therefore, determining the authenticity of images has attracted increasing attention in many applications, such as image forensics [1], [2] and biometric anti-spoofing [3].…”

Section: Introductionmentioning

confidence: 99%

Identification of deep network generated images using disparities in color components

et al. 2020

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With the powerful deep network architectures, such as generative adversarial networks and variational autoencoders, large amounts of photorealistic images can be generated. The generated images, already fooling human eyes successfully, are not initially targeted for deceiving image authentication systems. However, research communities as well as public media show great concerns on whether these images would lead to serious security issues. In this paper, we address the problem of detecting deep network generated (DNG) images by analyzing the disparities in color components between real scene images and DNG images. Existing deep networks generate images in RGB color space and have no explicit constrains on color correlations; therefore, DNG images have more obvious differences from real images in other color spaces, such as HSV and YCbCr, especially in the chrominance components. Besides, the DNG images are different from the real ones when considering red, green, and blue components together. Based on these observations, we propose a feature set to capture color image statistics for detecting the DNG images. Moreover, three different detection scenarios in practice are considered and the corresponding detection strategies are designed. Extensive experiments have been conducted on face image datasets to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method is able to distinguish the DNG images from real ones with high accuracies. Index TermsImage generative model, generative adversarial networks, fake image identification, image statistics.

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“…Over the past few years, many approaches have been proposed in the field of blind image forensic technologies [1,2]. The existing methodologies can be roughly classified into three categories according to the forensic features used.…”

Section: Introductionmentioning

confidence: 99%

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

Zhu

Shen²,

Niu³

2019

Symmetry

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With the wide use of various image altering tools, digital image manipulation becomes very convenient and easy, which makes the detection of image originality and authenticity significant. Among various image tampering detection tools, double JPEG image compression detector, which is not sensitive to specific image tampering operation, has received large attention. In this paper, we propose an improved double JPEG compression detection method based on noise-free DCT (Discrete Cosine Transform) coefficients mixture histogram model. Specifically, we first extract the block-wise DCT coefficients histogram and eliminate the quantization noise which introduced by rounding and truncation operations. Then, for each DCT frequency, a posterior probability can be obtained by solving the DCT coefficients mixture histogram with a simplified model. Finally, the probabilities from all the DCT frequencies are accumulated to give the posterior probability of a DCT block being authentic or tampered. Extensive experimental results in both quantitative and qualitative terms prove the superiority of our proposed method when compared with the state-of-the-art methods.

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Digital image integrity – a survey of protection and verification techniques

Cited by 113 publications

References 95 publications

Edge–texture feature-based image forgery detection with cross-dataset evaluation

Edge–texture feature-based image forgery detection with cross-dataset evaluation

Identification of deep network generated images using disparities in color components

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

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