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1. A Study of Public Awareness and Perceptions for Enhancing Deepfake Detection Technologies

   Guy, Jericka; Muhammad, Jean (March 2025, The 2025 ADMI Symposium.)

   Deepfake technology presents a significant challenge to cybersecurity. These highly sophisticated AI-generated manipulations can compromise sensitive information and erode public trust, privacy, and security. This has led to broader societal impacts, including decreased trust and confidence in digital communications. This paper will discuss public knowledge, understanding, and perception of AI-generated deepfakes, which was obtained through an online survey that measured people's ability to identify video, audio, and images of deepfakes. The findings will highlight the public's knowledge and perception of deepfakes, the risks that deepfake media presents, and the vulnerabilities to detection and prevention. This awareness will lead to stronger defense strategies and enhanced cybersecurity measures that will ultimately enhance deepfake detection technology and strengthen overall cybersecurity measures that will effectively mitigate exploitation risks and safeguard personal and organizational interests. 

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2. Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward

   https://doi.org/10.1007/s10489-022-03766-z  

   Masood, Momina; Nawaz, Mariam; Malik, Khalid Mahmood; Javed, Ali; Irtaza, Aun; Malik, Hafiz (June 2022, Applied Intelligence)

   Easy access to audio-visual content on social media, combined with the availability of modern tools such as Tensorflow or Keras, and open-source trained models, along with economical computing infrastructure, and the rapid evolution of deep-learning (DL) methods have heralded a new and frightening trend. Particularly, the advent of easily available and ready to use Generative Adversarial Networks (GANs), have made it possible to generate deepfakes media partially or completely fabricated with the intent to deceive to disseminate disinformation and revenge porn, to perpetrate financial frauds and other hoaxes, and to disrupt government functioning. Existing surveys have mainly focused on the detection of deepfake images and videos; this paper provides a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation, and the methodologies used to detect such manipulations in both audio and video. For each category of deepfake, we discuss information related to manipulation approaches, current public datasets, and key standards for the evaluation of the performance of deepfake detection techniques, along with their results. Additionally, we also discuss open challenges and enumerate future directions to guide researchers on issues which need to be considered in order to improve the domains of both deepfake generation and detection. This work is expected to assist readers in understanding how deepfakes are created and detected, along with their current limitations and where future research may lead. 

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3. Characterizing the Impact of Audio Deepfakes in the Presence of Cochlear Implant

   https://doi.org/10.14722/ndss.2025.241117  

   Pasternak, Magdalena; Warren, Kevin; Olszewski, Daniel; Nittrouer, Susan; Traynor, Patrick; Butler, Kevin (January 2025, Internet Society)

   Cochlear implants (CIs) allow deaf and hard-ofhearing individuals to use audio devices, such as phones or voice assistants. However, the advent of increasingly sophisticated synthetic audio (i.e., deepfakes) potentially threatens these users. Yet, this population’s susceptibility to such attacks is unclear. In this paper, we perform the first study of the impact of audio deepfakes on CI populations. We examine the use of CI-simulated audio within deepfake detectors. Based on these results, we conduct a user study with 35 CI users and 87 hearing persons (HPs) to determine differences in how CI users perceive deepfake audio. We show that CI users can, similarly to HPs, identify text-to-speech generated deepfakes. Yet, they perform substantially worse for voice conversion deepfake generation algorithms, achieving only 67% correct audio classification. We also evaluate how detection models trained on a CI-simulated audio compare to CI users and investigate if they can effectively act as proxies for CI users. This work begins an investigation into the intersection between adversarial audio and CI users to identify and mitigate threats against this marginalized group. 

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4. Who Uses Bots? A Statistical Analysis of Bot Usage in Moderation Teams

   https://doi.org/10.1145/3334480.3382960  

   Kiene, Charles; Hill, Benjamin Mako (April 2020, CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems)

   null (Ed.)

   Adopting new technology is challenging for volunteer moderation teams of online communities. Challenges are aggravated when communities increase in size. In a prior qualitative study, Kiene et al. found evidence that moderator teams adapted to challenges by relying on their experience in other technological platforms to guide the creation and adoption of innovative custom moderation "bots." In this study, we test three hypotheses on the social correlates of user innovated bot usage drawn from a previous qualitative study. We find strong evidence of the proposed relationship between community size and the use of user innovated bots. Although previous work suggests that smaller teams of moderators will be more likely to use these bots and that users with experience moderating in the previous platform will be more likely to do so, we find little evidence in support of either proposition. 

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5. "Better Be Computer or I'm Dumb": A Large-Scale Evaluation of Humans as Audio Deepfake Detectors

   https://doi.org/10.1145/3658644.3670325  

   Warren, Kevin; Tucker, Tyler; Crowder, Anna; Olszewski, Daniel; Lu, Allison; Fedele, Caroline; Pasternak, Magdalena; Layton, Seth; Butler, Kevin; Gates, Carrie; et al (December 2024, ACM)

   Audio deepfakes represent a rising threat to trust in our daily communications. In response to this, the research community has developed a wide array of detection techniques aimed at preventing such attacks from deceiving users. Unfortunately, the creation of these defenses has generally overlooked the most important element of the system - the user themselves. As such, it is not clear whether current mechanisms augment, hinder, or simply contradict human classification of deepfakes. In this paper, we perform the first large-scale user study on deepfake detection. We recruit over 1,200 users and present them with samples from the three most widely-cited deepfake datasets. We then quantitatively compare performance and qualitatively conduct thematic analysis to motivate and understand the reasoning behind user decisions and differences from machine classifications. Our results show that users correctly classify human audio at significantly higher rates than machine learning models, and rely on linguistic features and intuition when performing classification. However, users are also regularly misled by pre-conceptions about the capabilities of generated audio (e.g., that accents and background sounds are indicative of humans). Finally, machine learning models suffer from significantly higher false positive rates, and experience false negatives that humans correctly classify when issues of quality or robotic characteristics are reported. By analyzing user behavior across multiple deepfake datasets, our study demonstrates the need to more tightly compare user and machine learning performance, and to target the latter towards areas where humans are less likely to successfully identify threats. 

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