Detection of Digital Facial Retouching utilizing Face Beauty Information

2025-12-09

AI Summary

Digital facial beautification undermines biometric systems, posing security risks in social media and identity authentication. This paper proposes the first facial beautification detection framework grounded in aesthetic perception, integrating aesthetic assessment with deep learning to extract multi-dimensional aesthetic-semantic joint features—enabling generalizable detection without reliance on specific beautification algorithms. Departing from conventional approaches that solely exploit texture or artifact cues, our method innovatively models aesthetic alterations as discriminative indicators of beautification. In single-image detection, it achieves a 1.1% D-EER, substantially outperforming state-of-the-art methods. Experimental results validate that aesthetic information significantly enhances detection robustness and accuracy. This work establishes a novel paradigm for combating image manipulation and strengthening the reliability of facial recognition systems.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Safety and Robustness
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

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.

Problem

• Detects digital facial retouching in images
• Improves retouching detection using AI and beauty assessment
• Addresses security risks in biometric systems from retouched faces

Innovation

• Utilizes beauty assessment algorithms for retouching detection
• Employs AI-based feature extraction to improve detection accuracy
• Achieves low error rate with unknown retouching algorithms