Fooled twice: People cannot detect deepfakes but think they can
Ontology highlight
ABSTRACT:
SUBMITTER: Kobis N
PROVIDER: S-EPMC8602050 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Speech deepfakes are artificial voices generated by machine learning models. Previous literature has highlighted deepfakes as one of the biggest security threats arising from progress in artificial intelligence due to their potential for misuse. However, studies investigating human detection capabilities are limited. We presented genuine and deepfake audio to n = 529 individuals and asked them to identify the deepfakes. We ran our experiments in English and Mandarin to understand if language affects detection performance and decision-making rationale. We found that detection capability is unreliable. Listeners only correctly spotted the deepfakes 73% of the time, and there was no difference in detectability between the two languages. Increasing listener awareness by providing examples of speech deepfakes only improves results slightly. As speech synthesis algorithms improve and become more realistic, we can expect the detection task to become harder. The difficulty of detecting speech deepfakes confirms their potential for misuse and signals that defenses against this threat are needed.
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Project description:A deepfake is content or material that is synthetically generated or manipulated using artificial intelligence (AI) methods, to be passed off as real and can include audio, video, image, and text synthesis. The key difference between manual editing and deepfakes is that deepfakes are AI generated or AI manipulated and closely resemble authentic artifacts. In some cases, deepfakes can be fabricated using AI-generated content in its entirety. Deepfakes have started to have a major impact on society with more generation mechanisms emerging everyday. This article makes a contribution in understanding the landscape of deepfakes, and their detection and generation methods. We evaluate various categories of deepfakes especially in audio. The purpose of this survey is to provide readers with a deeper understanding of (1) different deepfake categories; (2) how they could be created and detected; (3) more specifically, how audio deepfakes are created and detected in more detail, which is the main focus of this paper. We found that generative adversarial networks (GANs), convolutional neural networks (CNNs), and deep neural networks (DNNs) are common ways of creating and detecting deepfakes. In our evaluation of over 150 methods, we found that the majority of the focus is on video deepfakes, and, in particular, the generation of video deepfakes. We found that for text deepfakes, there are more generation methods but very few robust methods for detection, including fake news detection, which has become a controversial area of research because of the potential heavy overlaps with human generation of fake content. Our study reveals a clear need to research audio deepfakes and particularly detection of audio deepfakes. This survey has been conducted with a different perspective, compared to existing survey papers that mostly focus on just video and image deepfakes. This survey mainly focuses on audio deepfakes that are overlooked in most of the existing surveys. This article's most important contribution is to critically analyze and provide a unique source of audio deepfake research, mostly ranging from 2016 to 2021. To the best of our knowledge, this is the first survey focusing on audio deepfakes generation and detection in English.
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Project description:Mate value is an important concept in mate choice research although its operationalization and understanding are limited. Here, we reviewed and evaluated previously established conceptual and methodological approaches measuring mate value and presented original research using individual differences in how people view themselves as a face-valid proxy for mate value in long- and short-term contexts. In data from 41 nations (N = 3895, Mage = 24.71, 63% women, 47% single), we tested sex, age, and relationship status effects on self-perceived mate desirability, along with individual differences in the Dark Triad traits, life history strategies, peer-based comparison of desirability, and self-reported mating success. Both sexes indicated more short-term than long-term mate desirability; however, men reported more long-term mate desirability than women, whereas women reported more short-term mate desirability than men. Further, individuals who were in a committed relationship felt more desirable than those who were not. Concerning the cross-sectional stability of mate desirability across the lifespan, in men, short- and long-term desirability rose to the age of 40 and 50, respectively, and decreased afterward. In women, short-term desirability rose to the age of 38 and decreased afterward, whereas long-term desirability remained stable over time. Our results suggest that measuring long- and short-term self-perceived mate desirability reveals predictable correlates.
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Project description:Botulinum neurotoxins (BoNTs) are a family of protein toxins consisting of seven known serotypes (BoNT/A-BoNT/G) and multiple subtypes within the serotypes, and all of which cause the disease botulism-a disease of great public health concern. Accurate detection of BoNTs in human clinical samples is therefore an important public health goal. To achieve this goal, our laboratory developed a mass spectrometry-based assay detecting the presence of BoNT via its enzymatic activity on a peptide substrate. Recently, publications reported the use of new peptide substrates to detect BoNT/A and /B with improved results over other peptide substrates. However, the authors did not provide results of their peptide substrate on multiple subtypes of BoNT. In this work, we describe the results of testing the new substrates with multiple BoNT/A and /B subtypes and find that the substrates cannot detect many subtypes of BoNT/A and /B.
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