DEFACTO: Image and Face Manipulation Dataset

Mahfoudi, Gaël; Tajini, Badr; Retraint, Florent; Morain-Nicolier, Frédéric; Dugelay, Jean Luc; Pic, Marc

doi:10.23919/eusipco.2019.8903181

Cited by 77 publications

(48 citation statements)

References 19 publications

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“…The results show that the proposed detector is very effective in localizing face manipulations. These values are significantly larger than those of the state-of-the-art forgery detection algorithms, reported in [44]. The images shown in Fig.…”

Section: Forgery Detection and Localizationmentioning

confidence: 66%

“…On the other hand, in the case of misaligned forgery, the proposed method is able to localize the forgery accurately, but the B. Li's method fails. [38] 10,000 Never-compressed RAISE [39] 8,156 Never-compressed HDR [40] 3,060 JPEG compressed VISION [41] 7,565 JPEG compressed DRESDEN [42] 16,961 JPEG compressed PS-Battles [43] 14,754 Forged DEFACTO [44] 79,600 Forged (face manipulation)…”

Section: B Comparison With State-of-the-art Jpeg Compression and Resampling Detectorsmentioning

confidence: 99%

“…We now evaluate the efficacy of the proposed detector in detecting and localizing real-life forgeries. The two datasets that we consider for this evaluation are the PS-Battles [43] and the DEFACTO [44] datasets. The PS-Battles dataset is created by gathering images from the popular photoshopbattles subreddit, which consists of a large community (about 12 million) of image manipulation enthusiasts.…”

Section: Forgery Detection and Localizationmentioning

confidence: 99%

“…We have also evaluated the forgery localization performance of the proposed detector on the images in the DEFACTO [44] dataset. The DEFACTO dataset consists of 2, 29, 000 forged images, all stored as bitmap images.…”

Section: Forgery Detection and Localizationmentioning

confidence: 99%

“…Average values of precision, recall and F1-score for face swap and face morph images in DEFACTO dataset[44].…”

mentioning

confidence: 99%

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A JPEG Forensic Detector for Color Bitmap Images

Kumawat

Pankajakshan

2021

IEEE Open J. Signal Process.

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Identification of decompressed JPEG images, especially those compressed with high JPEG quality factors, is a challenging issue in image forensics. Furthermore, the applicability of the existing JPEG forensic detectors in forgery localization is limited by their inability to cope with spatial misalignment in the 8 × 8 JPEG grid. This paper proposes a novel forensic detector for decompressed JPEG color images stored in the bitmap format. The basic idea is to exploit the chroma upsampling traces introduced during the JPEG decompression of color images. We first show that the chroma upsampling alters the wide-sense stationary nature of the chroma planes and introduces wide-sense cyclostationarity in the upsampled chroma planes of decompressed JPEG images. As a result, unlike in the case of never-compressed images, the distributions of the odd-even and even-odd pixel pair differences are different for the chroma planes of never-compressed and decompressed JPEG images. We use the two-sample Kolmogorov-Smirnov (KS) test to find the similarity between these distributions and thereby to differentiate between never-compressed and decompressed JPEG images. The experimental results on a large set of images, including realistically-forged images, show the efficacy of the proposed detector even in the presence of spatial desynchronization due to image cropping.

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Section: Forgery Detection and Localizationmentioning

confidence: 66%

Section: B Comparison With State-of-the-art Jpeg Compression and Resampling Detectorsmentioning

confidence: 99%

Section: Forgery Detection and Localizationmentioning

confidence: 99%

Section: Forgery Detection and Localizationmentioning

confidence: 99%

“…Average values of precision, recall and F1-score for face swap and face morph images in DEFACTO dataset[44].…”

mentioning

confidence: 99%

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A JPEG Forensic Detector for Color Bitmap Images

Kumawat

Pankajakshan

2021

IEEE Open J. Signal Process.

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Dual‐Stream Fusion and Multi‐scale Analysis: Introducing the Synergistic Dual‐Stream Network (SDS‐Net) for Image Manipulation Segmentation

Song,

Lin,

Xie

2024

Advanced Intelligent Systems

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This article introduces the synergistic dual‐stream network (SDS‐Net), a novel neural network architecture that significantly enhances the detection of image manipulations. SDS‐Net employs a unique dual‐stream fusion strategy that processes both RGB image and the corresponding noise map. It innovatively combines features computation from the different blocks of dual backbones, and leverages a multi‐scale spatial pyramid pooling (MSPP) module to expand the receptive fields of shallow features. This approach not only enriches the feature representation, but also ensures the precise localization of manipulated regions. Extensive experiments conducted on various public datasets demonstrate the superiority of SDS‐Net over several state‐of‐the‐art methods.

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Face Manipulation Detection in Remote Operational Systems

Pic

Mahfoudi

Trabelsi

et al. 2022

Handbook of Digital Face Manipulation and Detection

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In this chapter, we present the various categories of Face Manipulation and their use within different remote operational systems. We then use the example of remote identity document onboarding systems to illustrate how each category can be used in practice to compromise such a system. After a definition of the different Face Manipulation categories and the common algorithms used to produce them, we go through the various manipulation detection algorithms and common image and video forgery datasets. We then introduce some known counter-forensics methods that can be used by an attacker to avoid detection. Knowing the detection methods and the counter-forensic, we present how we can build up a safer system by using the correct methods at the correct time. But also how knowledge about the tampering process could be used to design the user experience to make the systems harder to compromise. We complete this review by the standardisation effort and legal aspect on the matter. And we conclude by discussing the remaining challenges and perspectives for better use of nowadays detection methods in practical usage.

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DEFACTO: Image and Face Manipulation Dataset

Cited by 77 publications

References 19 publications

A JPEG Forensic Detector for Color Bitmap Images

A JPEG Forensic Detector for Color Bitmap Images

Dual‐Stream Fusion and Multi‐scale Analysis: Introducing the Synergistic Dual‐Stream Network (SDS‐Net) for Image Manipulation Segmentation

Face Manipulation Detection in Remote Operational Systems

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