PRNU‐based detection of facial retouching

Rathgeb, Christian; Botaljov, Angelika; Stockhardt, Fabian; Isadskiy, Sergey; Debiasi, Luca; Uhl, Andreas; Busch, Christoph

doi:10.1049/iet-bmt.2019.0196

Cited by 40 publications

(34 citation statements)

References 38 publications

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“…• In a single image scenario, in which only a potentially manipulated image is processed by the detection system, the detection performance can highly depend on the quality of the manipulated image as well as applied image post-processing. It was found that image compression can severely impact face recognition performance [35] as well as retouching detection methods [17], [19]. Similar effects are to be expected for other types of postprocessing, e.g.…”

Section: Related Worksupporting

confidence: 61%

“…Among other image manipulation techniques, such as geometric distortion or morphing, they reported significant performance degradation for various facial recognition systems after the application of strong facial retouching. These findings have been confirmed by Bharati et al [15], [16] while Rathgeb et al [19] showed that face recognition systems might be robust to the application of moderate facial retouching.…”

Section: Related Workmentioning

confidence: 58%

“…Obtained detection performance rates are shown in Table 6. Lowest error rates are obtained by the approach scheme of [19] achieving rather high average D-EERs of 16.19% and 17.08% on the FRGCv2 and the FERET database, respectively. Similarly, the remaining schemes obtain average D-EERs slightly below 20%, except for the method of [56] which obtains results close to guessing.…”

Section: E Comparison With Single Image Detection Methodsmentioning

confidence: 95%

“…In the last experiment, the proposed system is compared to other published single image-based approaches in the challenging scenario where the potentially used retouching app is unknown. The following single image facial retouching detection methods have evaluated: a generic image forgery detection tool which aims at detecting inconsistencies in noise variances [56], the approach by Wang et al [18], Rathgeb et al [19], DFR, and TD (not that the FL-based method is not expected to reveal competitive results in a single image detection scenario). While the first scheme does not require any training, a pre-trained model is available for the scheme of [18].…”

Section: E Comparison With Single Image Detection Methodsmentioning

confidence: 99%

“…More recently, Wang et al [18] introduced a deep learningbased facial retouching detection scheme which is specifically designed to detect image warping operations performed using the Adobe Photoshop software. Rathgeb et al [19] proposed a facial retouching detection scheme which makes use of well-established image forensics techniques. Specifically, different spatial and spectral features extracted from Photo Response Non-Uniformity (PRNU) patterns across image regions are analyzed.…”

Section: Related Workmentioning

confidence: 99%

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Differential Detection of Facial Retouching: A Multi-Biometric Approach

et al. 2020

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Facial retouching apps have become common tools which are frequently applied to improve one's facial appearance, e.g. before sharing face images via social media. Beautification induced by retouching has the ability to substantially alter the appearance of face images and hence might represent a challenge for face recognition. Towards deploying secure face recognition as well as enforcing antiphotoshop legislations, a robust and reliable detection of retouched face image is needed. Published approaches consider a single image-based (no-reference) scenario where a potentially retouched face image serves as sole input to the retouching detector. However, in many cases a trusted unaltered face image of a subject examined is available which enables an image pair-based (differential) detection scheme. In this work, ICAO-compliant subsets of the FERET and FRGCv2 face databases are used to automatically create a database containing 9,078 retouched face images together with unconstrained probe images. In evaluations employing the commercial Cognitec FaceVACS and the open-source ArcFace face recognition system, it is shown that facial retouching can negatively impact face recognition performance. Further, a differential facial retouching detection system is proposed which processes pairs of a potentially retouched reference image and corresponding unaltered probe image of single subjects. Estimated differences in feature vectors obtained from texture descriptors, facial landmarks, and deep face representations are leveraged by machine learning-based classifiers of which the detection scores are fused to distinguish between retouched and unaltered face images. The proposed scheme is evaluated in a cross-database scenario where training and testing are performed on the FERET and FRGCv2 databases and vice versa. In the scenario where the used retouching algorithm is known by the detection algorithm, a competitive average D-EER of approximately 2% is achieved. Further, the scenario in which the employed retouching algorithm is not known by the detection algorithm is evaluated. In the latter scenario, the proposed approach obtains an average D-EER below 10% and is shown to outperform several state-of-the-art single image-based detection schemes.

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Section: Related Worksupporting

confidence: 61%

Section: Related Workmentioning

confidence: 58%

Section: E Comparison With Single Image Detection Methodsmentioning

confidence: 95%

Section: E Comparison With Single Image Detection Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Differential Detection of Facial Retouching: A Multi-Biometric Approach

et al. 2020

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Facial Retouching and Alteration Detection

Majumdar

Vatsa

Singh

2022

Handbook of Digital Face Manipulation and Detection

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On the social media platforms, the filters for digital retouching and face beautification have become a common trend. With the availability of easy-to-use image editing tools, the generation of altered images has become an effortless task. Apart from this, advancements in the Generative Adversarial Network (GAN) leads to creation of realistic facial images and alteration of facial images based on the attributes. While the majority of these images are created for fun and beautification purposes, they may be used with malicious intent for negative applications such as deepnude or spreading visual fake news. Therefore, it is important to detect digital alterations in images and videos. This chapter presents a comprehensive survey of existing algorithms for retouched and altered image detection. Further, multiple experiments are performed to highlight the open challenges of alteration detection.

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Digital Face Manipulation in Biometric Systems

Ibsen

Rathgeb

Fischer

et al. 2022

Advances in Computer Vision and Pattern Recognition

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Biometric technologies, in particular facerecognition, are employed in many personal, commercial, and governmental identity management systems around the world. The processing of digitally manipulated face images within a face recognition system may lead to false decisions and thus decrease the reliability of the decision system. This necessitates the development of manipulation detection modules which can be seamlessly integrated into the processing chain of face recognition systems. This chapter discusses the impact of face image manipulation on face recognition technologies. To this end, the basic processes and key components of biometric systems are briefly introduced with particular emphasis on facial recognition. Additionally, face manipulation detection scenarios and concepts of how to integrate detection methods to face recognition systems are discussed. In an experimental evaluation, it is shown that different types of face manipulation, i.e. retouching, face morphing, and swapping, can significantly affect the biometric performance of face recognition systems and hence impair their security. Eventually, this chapter provides an outlook on issues and challenges that face manipulation poses to face recognition technologies.

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PRNU‐based detection of facial retouching

Cited by 40 publications

References 38 publications

Differential Detection of Facial Retouching: A Multi-Biometric Approach

Differential Detection of Facial Retouching: A Multi-Biometric Approach

Facial Retouching and Alteration Detection

Digital Face Manipulation in Biometric Systems

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