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1. Prioritization in visual attention does not work the way you think it does.

   https://doi.org/10.1037/xhp0000887  

   Ng, Gavin J.; Buetti, Simona; Patel, Trisha N.; Lleras, Alejandro (February 2021, Journal of Experimental Psychology: Human Perception and Performance)

   null (Ed.)
2. Keep it Simple: How Visual Complexity and Preferences Impact Search Efficiency on Websites

   https://doi.org/10.1145/3313831.3376849  

   Baughan, Amanda; August, Tal; Yamashita, Naomi; Reinecke, Katharina (January 2020, Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)
3. Keep it Simple: How Visual Complexity and Preferences Impact Search Efficiency on Websites

   Amanda Baughan, Tal August (April 2020, Human factors in computing systems)

   Past research has shown that people prefer different levels of visual complexity in websites: While some prefer simple websites with little text and few images, others prefer highly complex websites with many colors, images, and text. We investigated whether users’ visual preferences reflect which website complexity they can work with most efficiently. We conducted an online study with 165 participants in which we tested their search efficiency and information recall. We confirm that the visual complexity of a website has a significant negative effect on search efficiency and information recall. However, the search efficiency of those who preferred simple websites was more negatively affected by highly complex websites than those who preferred high visual complexity. Our results suggest that diverse visual preferences need to be accounted for when assessing search response time and information recall in HCI experiments, testing software, or A/B tests. 

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4. It doesn't look like anything to me: using diffusion model to subvert visual phishing detectors

   Hao, Qingying; Diwan, Nirav; Yuan, Ying; Apruzzese, Giovanni; Conti, Mauro; Wang, Gang (August 2024, 33rd USENIX Security Symposium (USENIX Security 24))

   Visual phishing detectors rely on website logos as the invariant identity indicator to detect phishing websites that mimic a target brand's website. Despite their promising performance, the robustness of these detectors is not yet well understood. In this paper, we challenge the invariant assumption of these detectors and propose new attack tactics, LogoMorph, with the ultimate purpose of enhancing these systems. LogoMorph is rooted in a key insight: users can neglect large visual perturbations on the logo as long as the perturbation preserves the original logo's semantics. We devise a range of attack methods to create semantic-preserving adversarial logos, yielding phishing webpages that bypass state-of-the-art detectors. For text-based logos, we find that using alternative fonts can help to achieve the attack goal. For image-based logos, we find that an adversarial diffusion model can effectively capture the style of the logo while generating new variants with large visual differences. Practically, we evaluate LogoMorph with white-box and black-box experiments and test the resulting adversarial webpages against various visual phishing detectors end-to-end. User studies (n= 150) confirm the effectiveness of our adversarial phishing webpages on end users (with a detection rate of 0.59, barely better than a coin toss). We also propose and evaluate countermeasures, and share our code. 

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5. It Doesn't Look Like Anything to Me: Using Diffusion Model to Subvert Visual Phishing Detectors

   Hao, Qingying; Diwan, Nirav; Yuan, Ying; Apruzzese, Giovanni; Conti, Mauro; Wang, Gang (August 2024, Proceedings of The 33rd USENIX Security Symposium (USENIX Security))