Pub Date : 2024-02-29DOI: 10.1109/TTS.2024.3371786
Michael Eldred
By remaining blind to an invisible global social value, we are unable to face up to the challenges posed by today’s world. We do not ask insistently enough who we are, remaining content with traditional answers starting with those inherited from the Greeks: we are a species of animal, the rational animal, a social animal, a political animal, and proceeding to later answers in the modern age, including: we are individual subjects endowed with inner consciousness or free subjects with innate individual rights. This paper aims to lift the veil on some of our Western delusions about individual freedom in private-property owning, more or less liberal democracies in a globalized world — a world whose movement is constrained by a ceaseless, subterranean, circular, movement of thingfied value that is never adequately conceived and named as such. To conceptualize it as such, and thus bring it to light, is the task of hermeneutic phenomenology. Doing so reveals that we are not free subjects, but players in a game whose movements we only darkly surmise, reduced to mere character masks in a farce.
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Pub Date : 2024-02-16DOI: 10.1109/TTS.2024.3365421
Ying Xu;Philipp Terhörst;Marius Pedersen;Kiran Raja
In recent years, image and video manipulations with Deepfake have become a severe concern for security and society. Many detection models and datasets have been proposed to detect Deepfake data reliably. However, there is an increased concern that these models and training databases might be biased and, thus, cause Deepfake detectors to fail. In this work, we investigate factors causing biased detection in public Deepfake datasets by (a) creating large-scale demographic and non-demographic attribute annotations with 47 different attributes for five popular Deepfake datasets and (b) comprehensively analysing attributes resulting in AI-bias of three state-of-the-art Deepfake detection backbone models on these datasets. The analysis shows how various attributes influence a large variety of distinctive attributes (from over 65M labels) on the detection performance which includes demographic (age, gender, ethnicity) and non-demographic (hair, skin, accessories, etc.) attributes. The results examined datasets show limited diversity and, more importantly, show that the utilised Deepfake detection backbone models are strongly affected by investigated attributes making them not fair across attributes. The Deepfake detection backbone methods trained on such imbalanced/biased datasets result in incorrect detection results leading to generalisability, fairness, and security issues. Our findings and annotated datasets will guide future research to evaluate and mitigate bias in Deepfake detection techniques. The annotated datasets and the corresponding code are publicly available. The code link is: https://github.com/xuyingzhongguo/DeepFakeAnnotations