Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching

TL;DR

This study investigates how image compression affects face image manipulation detection, finding that deep face representations are robust against compression, and in some cases, compression can even enhance detection accuracy.

Contribution

The paper introduces novel detection algorithms using texture descriptors and deep face representations, and provides a comprehensive analysis of compression effects on detection performance.

Findings

Deep face representations outperform texture-based methods under compression.

Compression can significantly impair texture descriptor-based detection.

In certain cases, compression improves manipulation detection accuracy.

Abstract

In the past years, numerous methods have been introduced to reliably detect digital face image manipulations. Lately, the generalizability of these schemes has been questioned in particular with respect to image post-processing. Image compression represents a post-processing which is frequently applied in diverse biometric application scenarios. Severe compression might erase digital traces of face image manipulation and hence hamper a reliable detection thereof. In this work, the effects of image compression on face image manipulation detection are analyzed. In particular, a case study on facial retouching detection under the influence of image compression is presented. To this end, ICAO-compliant subsets of two public face databases are used to automatically create a database containing more than 9,000 retouched reference images together with unconstrained probe images. Subsequently, reference images are compressed applying JPEG and JPEG 2000 at compression levels recommended for face image storage in electronic travel documents. Novel detection algorithms utilizing texture descriptors and deep face representations are proposed and evaluated in a single image and differential scenario. Results obtained from challenging cross-database experiments in which the analyzed retouching technique is unknown during training yield interesting findings: (1) most competitive detection performance is achieved for differential scenarios employing deep face representations; (2) image compression severely impacts the performance of face image manipulation detection schemes based on texture descriptors while methods utilizing deep face representations are found to be highly robust; (3) in some cases, the application of image compression might as well improve detection performance.