Malicious facial image processing and counter- Counter-measures: A review

Recent advances in visual sensor technologies and computer vision enable to create more and more efficient face recognition systems, and more generally speaking, facial image processing tools. Such new performances generate some ethical and societal concerns. In parallel, such advances, in deep learning for example, contribute to the proliferation of malicious digital manipulations. The main objectives for designing such attacks is to access resources illegally, to harm some individuals or to make ineffective some technologies. In this presentation, we give an overview of existing attacks and related techniques attached to some applications in the domains of biometrics (e.g. identity spoofing), of medias (e.g. fake news), of video surveillance (e.g. de-anonymization). Some recent works investigate potential counter-measures for detecting such attacks.