Detecting Photoshopped Faces by Scripting Photoshop

Wang, Shengyu; Wang, Oliver; Zhang, Richard; Owens, Andrew; Efros, Alexei A.

doi:10.1109/iccv.2019.01017

Cited by 100 publications

(79 citation statements)

References 35 publications

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“…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.…”

Section: Related Workmentioning

confidence: 99%

“…Besides retouching, further image manipulation techniques can be applied to digitally change the appearance of face images including replacement or reenactment [24], [25], which are frequently referred to as "face swapping" or "deep-fakes", and morphing [26], [27]. It was found that human observers achieve only low accuracy in detecting said types of face image manipulations [28], [29] including facial retouching [18]. Further, different benchmarks [29], [30] have been conducted to compare the performance of published detection schemes.…”

Section: Introductionmentioning

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: E Comparison With Single Image Detection Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differential Detection of Facial Retouching: A Multi-Biometric Approach

et al. 2020

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“…Moreover, the authors emphasized that image compression artefacts can cause a decrease in the detection accuracy, if they are only present in retouched images. Generally, detection accuracy is expected to degrade, if severe image compression is applied as post-processing as recently reported in the work of Wang et al [77]. The development of deep learning-based re-touching detection schemes is facilitated by the possibility to automatically generate a sufficient amount of training data.…”

Section: Facial Retouchingmentioning

confidence: 99%

Impact and Detection of Facial Beautification in Face Recognition: An Overview

2019

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Facial beautification induced by plastic surgery, cosmetics or retouching has the ability to substantially alter the appearance of face images. Such types of beautification can negatively affect the accuracy of face recognition systems. In this work, a conceptual categorisation of beautification is presented, relevant scenarios with respect to face recognition are discussed, and related publications are revisited. Additionally, technical considerations and trade-offs of the surveyed methods are summarized along with open issues and challenges in the field. This survey is targeted to provide a comprehensive point of reference for biometric researchers and practitioners working in the field of face recognition, who aim at tackling challenges caused by facial beautification.

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“…There is ongoing work on detecting image manipulations or post-processing. For example, Adobe recently developed new tools to detect Photoshopped images [76].…”

Section: Analysis Of Countermeasuresmentioning

confidence: 99%