Automated Image Forgery Detection through Classification of JPEG Ghosts

Zach, Fabian; Rieß, Christian; Angelopoulou, Elli

doi:10.1007/978-3-642-32717-9_19

Cited by 24 publications

(6 citation statements)

References 9 publications

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“…The ideas behind double compression detection techniques can potentially facilitate the localization of altered regions in images [3,9,21,22,38]. We categorize double compression detection methods into three groups according to footprints that are exploited for feature extraction purposes.…”

Section: Related Workmentioning

confidence: 99%

Quantization-Unaware Double JPEG Compression Detection

et al. 2015

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The current double JPEG compression detection techniques identify whether or not an JPEG image file has undergone the compression twice, by knowing its embedded quantization table. This paper addresses another forensic scenario in which the quantization table of a JPEG file is not explicitly or reliably known, which may compel the forensic analyst to blindly reveal the recompression clues. To do this, we first statistically analyze the theory behind quantized alternating current (AC) modes in JPEG compression and show that the number of quantized AC modes required to detect double compression is a function of both the image's block texture and the compression's quality level in a fresh formulation. Consequently, a new double compression detection algorithm is proposed that exploits footprints introduced by all non-zero and zero AC modes based on Benford's law in a low-dimensional representation via PCA. Then, some evaluation frameworks are constructed to assess the robustness and generalization of the proposed method on various textured images belonging to three standard databases as well as different compression quality level settings. The average F 1 -measure score on all tested databases in the proposed method is about 74 % much better than the state-of-theart performance of 67.7 %. The proposed algorithm is also applicable to detect double compression from a JPEG file and localize tampered regions in actual image forgery scenarios. An implementation of our algorithms and used databases are available upon request to fellow researchers.

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Section: Related Workmentioning

confidence: 99%

Quantization-Unaware Double JPEG Compression Detection

et al. 2015

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“…Popescu et al [3] devised a method for forgery detection by dividing the image into several blocks, applying the PCA transform (for dimension reduction) and detecting the forgery by detecting similarity between the blocks. Zach et al [4] devised a method of forgery detection through classification of JPEG ghost.Hao-Chiang Hsu et al proposed copymove forgery detection by detecting the similarity using feature extraction by Gabor filter [5]. Leida Li et al [6] devised a method for copy-move forgery detection using Local Binary Patterns.…”

Section: Related Workmentioning

confidence: 99%

Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier

Hashmi¹,

Keskar²

2015

IJSIA

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“…forged. Currently, there are many techniques to spot forged regions [7][8][9][10][11][12][13][14][15] that exploits the artefacts left by multiple JPEG compression and other techniques of image manipulation to sight the forged regions. Camera primarily based ways [16] have additionally analyzed where the detection relies on demosaicing regularity or sensing element pattern noise wherever the irregularities of the sensing element patterns area unit extracted and compared for anomalies [17].…”

Section: Introductionmentioning

confidence: 99%

Image forgery detection based on fusion of lightweight deep learning models

Doegar¹,

Hiriyannaiah²,

Matt³

et al. 2021

Turk J Elec Eng & Comp Sci

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Image forgery detection is one of the key challenges in various real time applications, social media and online information platforms. The conventional methods of detection based on the traces of image manipulations are limited to the scope of predefined assumptions like hand-crafted features, size and contrast. In this paper, we propose a fusion based decision approach for image forgery detection. The fusion of decision is based on the lightweight deep learning models namely SqueezeNet, MobileNetV2 and ShuffleNet. The fusion decision system is implemented in two phases. First, the pretrained weights of the lightweight deep learning models are used to evaluate the forgery of the images. Secondly, the fine-tuned weights are used to compare the results of the forgery of the images with the pre-trained models. The experimental results suggest that the fusion based decision approach achieves better accuracy as compared to the state-of-the-art approaches.

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Automated Image Forgery Detection through Classification of JPEG Ghosts

Cited by 24 publications

References 9 publications

Quantization-Unaware Double JPEG Compression Detection

Quantization-Unaware Double JPEG Compression Detection

Image Forgery Authentication and Classification using Hybridization of HMM and SVM Classifier

Image forgery detection based on fusion of lightweight deep learning models

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