Detection of Digital Facial Retouching utilizing Face Beauty Information

• URL: http://arxiv.org/abs/2512.08397v1
• Date: Tue, 09 Dec 2025 09:23:33 GMT
• Title: Detection of Digital Facial Retouching utilizing Face Beauty Information
• Authors: Philipp Srock, Juan E. Tapia, Christoph Busch,
• Abstract summary: This work proposes to study and analyze changes in beauty assessment algorithms of retouched images.<n>It assesses different feature extraction methods based on artificial intelligence in order to improve retouching detection.<n>In a scenario where the attacking retouching algorithm is unknown, this work achieved 1.1% D-EER on single image detection.
• Score: 8.465162435003627
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Facial retouching to beautify images is widely spread in social media, advertisements, and it is even applied in professional photo studios to let individuals appear younger, remove wrinkles and skin impurities. Generally speaking, this is done to enhance beauty. This is not a problem itself, but when retouched images are used as biometric samples and enrolled in a biometric system, it is one. Since previous work has proven facial retouching to be a challenge for face recognition systems,the detection of facial retouching becomes increasingly necessary. This work proposes to study and analyze changes in beauty assessment algorithms of retouched images, assesses different feature extraction methods based on artificial intelligence in order to improve retouching detection, and evaluates whether face beauty can be exploited to enhance the detection rate. In a scenario where the attacking retouching algorithm is unknown, this work achieved 1.1% D-EER on single image detection.

Related papers

• Label-guided Facial Retouching Reversion [8.01225897515609]
  We propose a framework, dubbed Re-Face, to tackle the problem of facial retouching reversion.<n>It consists of a facial retouching detector, an image reversion model named FaceR, and a color correction module called Hierarchical Adaptive Instance Normalization (H-AdaIN)
  arXiv  Detail & Related papers  (2024-04-22T13:49:42Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• FACE-AUDITOR: Data Auditing in Facial Recognition Systems [24.082527732931677]
  Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images. To prevent the face images from being misused, one straightforward approach is to modify the raw face images before sharing them. We propose a complete toolkit FACE-AUDITOR that can query the few-shot-based facial recognition model and determine whether any of a user's face images is used in training the model.
  arXiv  Detail & Related papers  (2023-04-05T23:03:54Z)
• Robustness Disparities in Face Detection [64.71318433419636]
  We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models. Across all the datasets and systems, we generally find that photos of individuals who are $textitmasculine presenting$, of $textitolder$, of $textitdarker skin type$, or have $textitdim lighting$ are more susceptible to errors than their counterparts in other identities.
  arXiv  Detail & Related papers  (2022-11-29T05:22:47Z)
• Super-Resolution for Selfie Biometrics: Introduction and Application to Face and Iris [67.74999528342273]
  Lack of resolution has a negative impact on the performance of image-based biometrics. Super-resolution techniques have to be adapted for the particularities of images from a specific biometric modality. This chapter presents an overview of recent advances in super-resolution reconstruction of face and iris images.
  arXiv  Detail & Related papers  (2022-04-12T10:28:31Z)
• Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition [14.63266615325105]
  We propose a generator for automatically adding realistic tattoos to facial images. We show that it is possible to remove facial tattoos from real images without degrading the quality of the image. We also show that it is possible to improve face recognition accuracy by using the proposed deep learning-based tattoo removal.
  arXiv  Detail & Related papers  (2022-02-10T19:35:28Z)
• On the Effect of Selfie Beautification Filters on Face Detection and Recognition [53.561797148529664]
  Social media image filters modify the image contrast or illumination or occlude parts of the face with for example artificial glasses or animal noses. We develop a method to reconstruct the applied manipulation with a modified version of the U-NET segmentation network. From a recognition perspective, we employ distance measures and trained machine learning algorithms applied to features extracted using a ResNet-34 network trained to recognize faces.
  arXiv  Detail & Related papers  (2021-10-17T22:10:56Z)
• State of the Art: Face Recognition [0.0]
  Document presents a short review face recognition methods for images with natural and eye occlude faces. The purpose is to select the best baseline approach for solving automatic face recognition of occluded faces.
  arXiv  Detail & Related papers  (2021-08-26T14:37:29Z)
• Impact of Facial Tattoos and Paintings on Face Recognition Systems [14.784088881975897]
  We investigate the impact that facial tattoos and paintings have on current face recognition systems. The impact on these modules was evaluated using state-of-the-art open-source and commercial systems. Our work is an initial case-study and indicates a need to design algorithms which are robust to the visual changes caused by facial tattoos and paintings.
  arXiv  Detail & Related papers  (2021-03-17T22:38:13Z)
• Facial Expressions as a Vulnerability in Face Recognition [73.85525896663371]
  This work explores facial expression bias as a security vulnerability of face recognition systems. We present a comprehensive analysis of how facial expression bias impacts the performance of face recognition technologies.
  arXiv  Detail & Related papers  (2020-11-17T18:12:41Z)
• Cosmetic-Aware Makeup Cleanser [109.41917954315784]
  Face verification aims at determining whether a pair of face images belongs to the same identity. Recent studies have revealed the negative impact of facial makeup on the verification performance. This paper proposes a semanticaware makeup cleanser (SAMC) to remove facial makeup under different poses and expressions.
  arXiv  Detail & Related papers  (2020-04-20T09:18:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.