Image deraining is an image restoration task that aims at decomposing a rainy image into the clear scene layer and the rain layer. Most existing deraining methods use supervised learning and training on synthetic rainy-clear pairs under the assumption that the only difference between rain and non-rain images is the rain effect. Since scenes constantly change due to vehicle movement and other activities, this assumption nearly fails when applied to traffic data. In this work, we introduce a de-raining framework using contrastive learning based on Vision Transformer. The proposed framework utilizes weakly paired images, in which the difference between images is not only caused by rain. In addition, we collect a real-world deraining dataset from traffic surveillance cameras. This is the first real-world deraining dataset in the traffic domain. The experiments show that our method achieves competitive performance with other existing methods while taking less time in the inference process.
This paper presents a new hybrid deep-learning model, U-ReACH, for detecting ships over satellite images. Specifically, the new U-ReACH model combines two well-known deep learning models, Unet and ResNet. However, applying a deep-learning model straight into satellite imagery-related tasks usually leads to poor performance due to data imbalance. An Auxiliary Classification Head is added at the end of the feature extraction stage to overcome the issue. Besides, a new loss function is used to handle the problem of imbalanced data. The experiment results were executed on the Airbus Ship dataset, including 192,556 satellite images, which showed the promise of the new approach.
In the digital age, identifying forgery in images has become a significant challenge for image analysis systems and applications. In this study, we approach this issue from a different perspective compared to existing methods for this problem, proposing an attack method to forgery detection models. Instead of the traditional approach of randomly modifying images, we combine a range of diverse strategies, ranging from the utilization of diffusion models to the implementation of camera trace erasing techniques aimed at eliminating the remnants left by forgers, consequently leading to the deception of forensic methods. This is not a simple problem, it requires a strategy to choose which models and in what order is most appropriate to both attack effectively and ensure that image quality is not reduced. Experimental results demonstrate that our method not only surpasses modern counter-forensics methods but also preserves the naturalness and semantics of the processed images. In this way, our research provides a valuable contribution to understanding and responding to the threats faced by modern image analysis systems.
In the digital age, the authenticity of photographs is increasingly questioned due to the rise of sophisticated AI and human manipulation techniques. This paper presents the development of an AI-assisted system designed to aid users in identifying and verifying the genuineness of photos. Unlike fully automated solutions, our approach emphasizes user empowerment, providing tools to enhance human expertise in photo authentication. The system integrates advanced algorithms to detect potential manipulations and localize regions of interest, directing users’ attention to specific areas that warrant closer examination. Users can conduct more efficient and effective authenticity checks without scrutinizing the entire image by focusing on these targeted regions. Our goal is to augment, not replace, the critical role of human judgment in verifying photo authenticity. The system’s design and implementation are discussed, along with case studies demonstrating its practical application in various scenarios. This research aims to contribute to the growing field of digital forensics by providing a robust, user-friendly tool that bridges the gap between AI capabilities and human expertise in photo verification.
Brain tumors pose a significant medical challenge, necessitating precise and rapid diagnosis for effective treatment and improved patient outcomes. This paper introduces knowledge distillation, which has the potential to revolutionize brain tumor diagnosis by enabling early identification from medical imaging data. Using a sophisticated teacher’ model to capture intricate patterns, we distill this knowledge into a more efficient "student’ model, aiming for comparable accuracy with reduced memory usage and improved inference times. Our method, based on a dataset of 357 MRI scans, demonstrated the potential of knowledge distillation in brain tumor diagnosis, offering a promising avenue for advancing patient care. The proposed model serves as a vital tool for healthcare practitioners, providing accurate and efficient support in detecting brain tumors and contributing to advancements in healthcare technology. The evaluation results indicate the effectiveness of our technique, achieving an impressive accuracy of 98.10
