Detection of Digital Facial Retouching utilizing Face Beauty Information

TL;DR

This paper investigates detecting digitally retouched facial images by analyzing beauty assessment changes and AI-based features, achieving a low error rate even with unknown retouching methods.

Contribution

It introduces a novel approach combining beauty assessment analysis and AI features to improve facial retouching detection accuracy.

Findings

Achieved 1.1% D-EER in single image detection

Analyzed effectiveness of beauty-based features for retouching detection

Demonstrated robustness against unknown retouching algorithms

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.