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   Baraheem, Samah S; Nguyen, Tam V (October 2023, Journal of Imaging)

   The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework for reliably detecting AI-generated images from real ones through Convolutional Neural Networks (CNNs). First, GAN-generated images were collected based on different tasks and different architectures to help with the generalization. Then, transfer learning was applied. Finally, several Class Activation Maps (CAM) were integrated to determine the discriminative regions that guided the classification model in its decision. Our approach achieved 100% on our dataset, i.e., Real or Synthetic Images (RSI), and a superior performance on other datasets and configurations in terms of its accuracy. Hence, it can be used as an evaluation tool in image generation. Our best detector was a pre-trained EfficientNetB4 fine-tuned on our dataset with a batch size of 64 and an initial learning rate of 0.001 for 20 epochs. Adam was used as an optimizer, and learning rate reduction along with data augmentation were incorporated. 

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2. Governance of the AI, by the AI, and for the AI

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3. From ”Explainable AI” to ”Graspable AI”

   https://doi.org/10.1145/3430524.3442704  

   Ghajargar, Maliheh; Bardzell, Jeffrey; Renner, Alison Smith; Krogh, Peter Gall; Höök, Kristina; Cuartielles, David; Boer, Laurens; Wiberg, Mikael (February 2021, Proceedings of the Fifteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI '21))

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4. AI for Security and Security for AI

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   Bertino, Elisa; Kantarcioglu, Murat; Akcora, Cuneyt Gurcan; Samtani, Sagar; Mittal, Sudip; Gupta, Maanak (April 2021, ACM Conference on Datta and Application Security and Privacy (CODASPY) 2021)

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5. Polite AI mitigates user susceptibility to AI hallucinations

   https://doi.org/10.1080/00140139.2024.2434604  

   Pak, Richard; Rovira, Ericka; McLaughlin, Anne (November 2024, Ergonomics)

   With their increased capability, AI-based chatbots have become increasingly popular tools to help users answer complex queries. However, these chatbots may hallucinate, or generate incorrect but very plausible-sounding information, more frequently than previously thought. Thus, it is crucial to examine strategies to mitigate human susceptibility to hallucinated output. In a between-subjects experiment, participants completed a difficult quiz with assistance from either a polite or neutral-toned AI chatbot, which occasionally provided hallucinated (incorrect) information. Signal detection analysis revealed that participants interacting with polite-AI showed modestly higher sensitivity in detecting hallucinations and a more conservative response bias compared to those interacting with neutral-toned AI. While the observed effect sizes were modest, even small improvements in users’ ability to detect AI hallucinations can have significant consequences, particularly in high-stakes domains or when aggregated across millions of AI interactions. 

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