Enhancing website security is crucial to combat malicious activities, and CAPTCHA (Completely Automated Public Turing tests to tell Computers and Humans Apart) has become a key method to distinguish humans from bots. While text-based CAPTCHAs are designed to challenge machines while remaining human-readable, recent advances in deep learning have enabled models to recognize them with remarkable efficiency. In this regard, we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention (GVA), which sharpens focus on relevant visual features. We have specifically adapted the well-established image captioning task to address this need. Our approach utilizes the first-level attention module as guidance to the second-level attention component, incorporating two LSTM (Long Short-Term Memory) layers to enhance CAPTCHA recognition. Our extensive evaluation across four diverse datasets—Weibo, BoC (Bank of China), Gregwar, and Captcha 0.3—shows the adaptability and efficacy of our method. Our approach demonstrated impressive performance, achieving an accuracy of 96.70% for BoC and 95.92% for Webo. These results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets, showcasing its robustness, reliability, and ability to handle varied challenges in CAPTCHA recognition.

Image captioning has seen significant research efforts over the last decade. The goal is to generate meaningful semantic sentences that describe visual content depicted in photographs and are syntactically accurate. Many real-world applications rely on image captioning, such as helping people with visual impairments to see their surroundings. To formulate a coherent and relevant textual description, computer vision techniques are utilized to comprehend the visual content within an image, followed by natural language processing methods. Numerous approaches and models have been developed to deal with this multifaceted problem. Several models prove to be state-of-the-art solutions in this field. This work offers an exclusive perspective emphasizing the most critical strategies and techniques for enhancing image caption generation. Rather than reviewing all previous image captioning work, we analyze various techniques that significantly improve image caption generation and achieve significant performance improvements, including encompassing image captioning with visual attention methods, exploring semantic information types in captions, and employing multi-caption generation techniques. Further, advancements such as neural architecture search, few-shot learning, multi-phase learning, and cross-modal embedding within image caption networks are examined for their transformative effects. The comprehensive quantitative analysis conducted in this study identifies cutting-edge methodologies and sheds light on their profound impact, driving forward the forefront of image captioning technology.

The modern university computer lab and kindergarden through 12th grade classrooms require a centralized solution to efficiently manage a large number of desktops. The existing solutions either bring virtualization overhead in runtime or requires loading a large image over 30 GB leading to an unacceptable network latency. In this work, we propose Troy which takes advantage of the differencing virtual hard disk techniques in Windows systems. As such, Troy only loads the modifications made on one machine to all other machines. Troy consists of two modules that are responsible to generate an initial image and merge a differencing image with its parent image, respectively. Specifically, we identify the key fields in the virtual hard disk image that links the differencing image and the parent image and find the modified blocks in the differencing images that should be used to replace the blocks in the parent image. We further design a lazy copy solution to reduce the I/O burden in image merging. We have implemented Troy on bare metal machines. The evaluation results show that the performance of Troy is comparable to the native implementation in Windows, without requiring the Windows environment.