# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 1 Introduction  
The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA), which is recognized as a Human Interactive Proof (HIP), represents a widely utilized mechanism designed to autonomously differentiate between human users and machines [1]. Introduced by [PERSON] and colleagues as a text-oriented CAPTCHA variant known as reCAPTCHA, this CAPTCHA emerged from a research initiative at Carnegie Mellon University in 2003 [2]. Due to its low cost, high reliability, and easy deployment, text-based CAPTCHA is widely used as a security product on the Internet and is applied to many websites and information systems around the world [3]. However, it is difficult for websites to distinguish whether the CAPTCHA reader is a human or a machine. These text CAPTCHAs are increasingly vulnerable to malicious attacks based on deep learning, such as the Deep CAPTCHA [4]. The pervasive incorporation of digital and Roman characters in text-based CAPTCHAs could render them susceptible to interpretation by Optical Character Recognition (OCR) [5]. For CAPTCHAs engendered with elevated levels of noise, the efficacy of models in deciphering the content is significantly impeded owing to the persistence of interference even after the images are binarized [6]. Breaking algorithms based on traditional machine learning, including Decision Trees (DT), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), also suffer from insufficient recognition ability [7]. The research on the CAPTCHA recognition algorithm can help websites and CAPTCHA designers find the vulnerabilities of CAPTCHAs. By using the proposed CAPTCHA recognition engines, companies and developers may evaluate the security of the website's CAPTCHAs and block dangerous vulnerabilities in time to avoid being attacked. From a commercial point of view, the CAPTCHA recognition algorithms can be embedded into network security assessment toolkits to evaluate the security of websites. In addition, the research on CAPTCHA recognition may be used for reference in scene text recognition, object detection, and OCR. In recent years, the field of text-based CAPTCHA recognition has seen several advancements, particularly in the realm of deep learning, which has become the mainstream direction for text-based CAPTCHA recognition [8]. CNNs are the most used networks as they excel at learning spatial hierarchies in an image, thereby providing a robust way for CAPTCHA recognition [9]. Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM) is another neural network that has shown impressive recognition accuracy in CAPTCHAs with Connecting Characters Together (CCT), as it can remember patterns over lengthy sequences [10]. Combined with CNNs and RNNs, the attention mechanisms allow the neural network to focus on certain important features, spatial positions, and channels, thereby improving the recognition effect [11]. Additionally, some content generation techniques have shown potential for enhancing models by creating believable CAPTCHA-like images to train a more robust network [12]. It is important to note that a single model may not always be the best choice. Depending on the complexity of the CAPTCHAs, a hybrid approach could be employed to decode CAPTCHAs [13]. The CAPTCHA recognition model is commonly utilized as a module by web scrawlers running on personal CPU platforms for data collection tasks. Therefore, it is necessary to reduce Parameters (PARAMs) while maintaining the Average Attack Success Rate (AASR). Accordingly, it is important to discern as many CAPTCHAs as possible per second. Frames Per Second (FPS) is therefore employed as a metric to gauge the real-time performance of CAPTCHA solvers, which reflects the number of CAPTCHAs that a solver can process each second, making it a critical measure of efficiency in some scenarios.