Open AccessProceedings Article
Image manipulation detection with Binary Similarity Measures
Sevinc Bayram,Ismail Avcibas,Bulent Sankur,Nasir Memon +3 more
- pp 1-4
TLDR
This paper proposes a method based on the neighbor bit planes of the image to distinguish the original images from the altered ones, the genuine ones from the doctored ones, and shows that the correlation between the bit planes as well the binary texture characteristics within the bits will differ between an original and a doctored image.Abstract:
Since extremely powerful technologies are now available to generate and process digital images, there is a concomitant need for developing techniques to distinguish the original images from the altered ones, the genuine ones from the doctored ones In this paper we focus on this problem and propose a method based on the neighbor bit planes of the image The basic idea is that, the correlation between the bit planes as well the binary texture characteristics within the bit planes will differ between an original and a doctored image This change in the intrinsic characteristics of the image can be monitored via the quantal-spatial moments of the bit planes These so-called Binary Similarity Measures are used as features in classifier design It has been shown that the linear classifiers based on BSM features can detect with satisfactory reliability most of the image doctoring executed via Photoshop toolread more
Citations
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Journal ArticleDOI
A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery
TL;DR: The problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward.
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Image forgery detection
TL;DR: The field of digital forensics has emerged to help restore some trust to digital images and the author reviews the state of the art in this new and exciting field.
Journal ArticleDOI
An Evaluation of Popular Copy-Move Forgery Detection Approaches
TL;DR: This paper created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation, and examined the 15 most prominent feature sets, finding the keypoint-based features Sift and Surf as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well.
Journal ArticleDOI
An Evaluation of Popular Copy-Move Forgery Detection Approaches
TL;DR: Wang et al. as mentioned in this paper examined the 15 most prominent feature sets and analyzed the detection performance on a per-image basis and on per-pixel basis, and found that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well.
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Digital image forgery detection using passive techniques: A survey
TL;DR: An attempt is made to survey the recent developments in the field of digital image forgery detection and a complete bibliography is presented on blind methods for forgery Detection.
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Higher-order Wavelet Statistics and their Application to Digital Forensics
Hany Farid,Siwei Lyu +1 more
TL;DR: A statistical model for natural images that is built upon a multi-scale wavelet decomposition that can be useful in several digital forensic applications, specifically in detecting various types of digital tampering.
Proceedings ArticleDOI
Image steganalysis with binary similarity measures
TL;DR: A novel technique for steganalysis of images that have been subjected to embedding by steganographic algorithms that is found to have complementary performance vis-à-vis Farid's scheme in that they outperform each other in alternate embedding techniques.