Knee osteoarthritis (KOA) is a degenerative joint disease that significantly impacts quality of life. While transfer learning shows promise in medical imaging, its application to KOA diagnosis remains challenging due to medical data's unique characteristics. To address this, we propose TRIKOP, a framework leveraging Visual Prompting for KOA diagnosis on MRI. Our approach explores three prompt-generating strategies that extract clinically relevant information from input images. Each prompt type is encoded using a tailored method to integrate effectively into the Vision Transformer for optimal representation. Among them, the contrastive embedding prompting strategy achieves 63.04% accuracy on the OAI dataset, surpassing prior studies. Moreover, TRIKOP produces attention maps highlighting diagnostically significant regions, improving model interpretability. This work highlights TRIKOP’s potential to improve AI-driven KOA diagnosis and clinical support.

Video games is one of the most popular multimedia forms and generate higher profits than the traditional film industry. In the meantime, with the advances of deep learning, computer vision algorithms have become more powerful for analyzing the video content and have been applied in the FPS video games as an advanced cheating tools, which have taken the video games industry by storm. Such algorithms, including the object detection and human pose estimations, could analyze and understand the video content in each frame and further help the player to automatically identify and aim at the enemies with extremely fast reaction. Compared to the classic cheating tools, computer-vision-based cheating tools are harder to detect and defend against because they do not need to manipulate the software or the system but purely simulate how a well trained and skilled human gamer plays the video game. In this paper, we propose a proactive and comprehensive defense approach, which generates perturbations that are not perceptible to humans yet can still mislead the computer vision algorithms. More specifically, this comprehensive approach includes two parts, the defense approach aims to fail the computer vision-based cheating tools to detect the in-game characters while the penalty approach aims to fool the computer vision-based cheating tools to detect the fake regions as in-game characters, which not only worsen the cheating experience but also serve as a trigger for detecting the cheating behavior. In this work, we first implement the object detection based cheating tools as the evaluation environment. Then, we implement our proposed defense, penalty and comprehensive approaches and evaluate the performance with four popular video games. The results show that our comprehensive approach obtains a high success rate with minor impact to user experience quality.