1-D DCT Domain Analysis for JPEG Double Compression Detection

Giudice, Oliver; Guarnera, Francesco; Paratore, A.; Battiato, Sebastiano

doi:10.1007/978-3-030-30645-8_65

Cited by 18 publications

(12 citation statements)

References 25 publications

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“…As regards implementations used for testing above mentioned techniques: the publicly available 3 Matlab implementation was employed for Bianchi et al [17]; code from the ICVGIP-2016.RAR archive available on Dr. Manish Okade's website 4 was employed for Dalmia et al [19]; models and implementation available on Github 5 were employed for Niu at al. [28] and finally an implementation from scratch was employed for Galvan et al [4] Experiments were carried out for standard tables and custom ones, all employing 64×64 patches extracted from RAISE dataset [32].…”

Section: Resultsmentioning

confidence: 99%

“…This makes forensics analysis really difficult in order to reconstruct the history of an image from the first acquisition device to each of the subsequent processing ( [2,3]). Even detecting if an investigated image has been compressed only two times is a challenging task, namely Double Compression Detection ( [4][5][6]). The problem is furtherly complicated by considering the possibility to crop and/or resize images (e.g., aligned and non-aligned scenario [7,8]).…”

Section: Introductionmentioning

confidence: 99%

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Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

Battiato

Giudice

Guarnera

et al. 2021

EURASIP J. on Info. Security

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The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a novel technique to estimate “previous” JPEG quantization factors on images compressed multiple times, in the aligned case by analyzing statistical traces hidden on Discrete Cosine Transform (DCT) histograms is exploited. Experimental results on double, triple and quadruple compressed images, demonstrate the effectiveness of the proposed technique while unveiling further interesting insights.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

Battiato

Giudice

Guarnera

et al. 2021

EURASIP J. on Info. Security

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show abstract

“…Indeed, some Deepfake images, in the Fourier domain, after being processed by a Discrete Fast Fourier Transform, show abnormal frequencies distributions. This preliminary insight was detected by Guarnera et al [4] in which the authors tried to roughly detect Deepfakes by means of well-known forgery detection tools ( [2], [15], [16]) with only few insights for future works as results. The analysis in the Fourier domain was employed by Zhang et al [17] in a rather naive strategy which delivered in any case good performances.…”

Section: B Deepfake Detection Methodsmentioning

confidence: 90%

Fighting Deepfake by Exposing the Convolutional Traces on Images

2020

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“…Indeed, some Deepfake images, in the Fourier domain, after being processed by a Discrete Fast Fourier Transform, show abnormal frequencies distributions. This preliminary insight was detected by Guarnera et al [4] in which the authors tried preliminarly to detect Deepfakes by means of well-known forgery detection tools ( [2], [15], [16]) with only few insights for future works as results. The analysis in the Fourier domain was employed more in deep by Zhang et al [17] in a naive strategy achieved good performances.…”

Section: B Deepfake Detection Methodsmentioning

confidence: 95%

Fighting Deepfake by Exposing the Convolutional Traces on Images

Guarnera,

Giudice,

Battiato

2020

Preprint

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Advances in Artificial Intelligence and Image Processing are changing the way people interacts with digital images and video. Widespread mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos such gender swap, aging, etc. The results are utterly realistic and extremely easy to be exploited even for non-experienced users. This kind of media object took the name of Deepfake and raised a new challenge in the multimedia forensics field: the Deepfake detection challenge. Indeed, discriminating a Deepfake from a real image could be a difficult task even for human eyes but recent works are trying to apply the same technology used for generating images for discriminating them with preliminary good results but with many limitations: employed Convolutional Neural Networks are not so robust, demonstrate to be specific to the context and tend to extract semantics from images. In this paper, a new approach aimed to extract a Deepfake fingerprint from images is proposed. The method is based on the Expectation-Maximization algorithm trained to detect and extract a fingerprint that represents the Convolutional Traces (CT) left by GANs during image generation. The CT demonstrates to have high discriminative power achieving better results than stateof-the-art in the Deepfake detection task also proving to be robust to different attacks. Achieving an overall classification accuracy of over 98%, considering Deepfakes from 10 different GAN architectures not only involved in images of faces, the CT demonstrates to be reliable and without any dependence on image semantic. Finally, tests carried out on Deepfakes generated by FACEAPP achieving 93% of accuracy in the fake detection task, demonstrated the effectiveness of the proposed technique on a real-case scenario.

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1-D DCT Domain Analysis for JPEG Double Compression Detection

Cited by 18 publications

References 25 publications

Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

Estimating Previous Quantization Factors on Multiple JPEG Compressed Images

Fighting Deepfake by Exposing the Convolutional Traces on Images

Fighting Deepfake by Exposing the Convolutional Traces on Images

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