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1. 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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2. Effects of Browsing Conditions and Visual Alert Design on Human Susceptibility to Deepfakes

   https://doi.org/10.54501/jots.v2i2.144  

   Josephs, Emilie; Fosco, Camilo; Oliva, Aude (February 2024, Journal of Online Trust and Safety)

   The increasing reach of deepfakes raises practical questions about people’s ability to detect false videos online. How vulnerable are people to deepfake videos? What technologies can help improve detection? Previous experiments that measure human deepfake detection historically omit a number of conditions that can exist in typical browsing conditions. Here, we operationalized four such conditions (low prevalence, brief presentation, low video quality, and divided attention), and found in a series of online experiments that all conditions lowered detection relative to baseline, suggesting that the current literature underestimates people’s susceptibility to deepfakes. Next, we examined how AI assistance could be integrated into the human decision process. We found that a model that exposes deepfakes by amplifying artifacts increases detection rates, and also leads to higher rates of incorporating AI feedback and higher final confidence than text-based prompts. Overall, this suggests that visual indicators that cause distortions on fake videos may be effective at mitigating the impact of falsified video. 

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3. 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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4. Cross-Platform Disinformation Campaigns: Lessons Learned and Next Steps

   https://doi.org/10.37016/mr-2020-002  

   Wilson, Tom; Starbird, Kate (January 2020, Harvard Kennedy School Misinformation Review)

   We conducted a mixed-method, interpretative analysis of an online, cross-platform disinformation campaign targeting the White Helmets, a rescue group operating in rebel-held areas of Syria that have become the subject of a persistent effort of delegitimization. This research helps to conceptualize what a disinformation campaign is and how it works. Based on what we learned from this case study, we conclude that a comprehensive understanding of disinformation requires accounting for the spread of content across platforms and that social media platforms should increase collaboration to detect and characterize disinformation campaigns. 

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5. Understanding the Use of Fauxtography on Social Media

   Wang, Yuping; Tahmasbi, Fatemeh; Blackburn, Jeremy; Bradlyn, Barry; De Cristofaro, Emiliano; Magerman, David; Zannettou, Savvas; Stringhini, Gianluca (June 2021, Proceedings of the International AAAI Conference on Weblogs and Social Media)

   Budak, Ceren; Cha, Meeyoung; Quercia, Daniele; Xie, Lexing (Ed.)

   Despite the influence that image-based communication has on online discourse, the role played by images in disinformation is still not well understood. In this paper, we present the first large-scale study of fauxtography, analyzing the use of manipulated or misleading images in news discussion on online communities. First, we develop a computational pipeline geared to detect fauxtography, and identify over 61k instances of fauxtography discussed on Twitter, 4chan, and Reddit. Then, we study how posting fauxtography affects engagement of posts on social media, finding that posts containing it receive more interactions in the form of re-shares, likes, and comments. Finally, we show that fauxtography images are often turned into memes by Web communities. Our findings show that effective mitigation against disinformation need to take images into account, and highlight a number of challenges in dealing with image-based disinformation. 

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