More Like this

1. AI vs. AI: Can AI Detect AI-Generated Images?

   https://doi.org/10.3390/jimaging9100199  

   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. 

   more » « less
2. Governance of the AI, by the AI, and for the AI

   Torrance, Andrew W; Tomlinson, Bill (September 2023, Mississippi Law Journal)
3. The Who in Explainable AI: How AI Background Shapes Perceptions of AI Explanations

   Ehsan, Upol; Passi, Samir; Liao, Q. Vera; Chan, Larry; Lee, I-Hsiang; Muller, Michael; Riedl, Mark O. (January 2021, arXiv)

   null (Ed.)
4. AI/ML curation of AI/ML training datasets

   https://doi.org/10.1117/12.3055515  

   Shukla, Shruti; Pados, Dimitris A; Varma, Kavita; Sklivanitis, George; Bentley, Elizabeth S; Medley, Michael J (May 2025, SPIE)

   Sklivanitis, George; Markopoulos, Panagiotis; Ouyang, Bing (Ed.)
5. Personality of AI

   Yu, Byunggu; Kim, Junwhan (June 2024, Springer LNCS)

   This research paper delves into the evolving landscape of fine-tuning large language models (LLMs) to align with human users, extending beyond basic alignment to propose "personality alignment" for language models in organizational settings. Acknowledging the impact of training methods on the formation of undefined personality traits in AI models, the study draws parallels with human fitting processes using personality tests. Through an original case study, we demonstrate the necessity of personality fine-tuning for AIs and raise intriguing questions about applying human-designed tests to AIs, engineering specialized AI personality tests, and shaping AI personalities to suit organizational roles. The paper serves as a starting point for discussions and developments in the burgeoning field of AI personality alignment, offering a foundational anchor for future exploration in human-machine teaming and co-existence. 

   more » « less