# Adaptive CAPTCHA: A CRNN-Based Text CAPTCHA Solver with Adaptive Fusion Filter Networks
## 2 Related Works
### CAPTCHA Recognition with CNN and RNN  
The most powerful classification networks, CNNs and RNNs, serve as the foundation for many CAPTCHA recognition networks. Leveraging the Dense Convolutional Network (DenseNet) with cross-layer connections, [PERSON] et al. introduced modified networks for CAPTCHA recognition [16]. They reduced the number of convolutional blocks and developed specific classifiers for different CAPTCHA image types. A CAPTCHA recognition method with a focal loss function is presented by [PERSON] et al. Their method enhances the traditional VGG network structure and incorporates the focal loss function [17]. [PERSON] et al. proposed a skip-connection CNN model using two publicly available datasets of text-based CAPTCHA images, which yields a promising result compared to previous studies [18]. A drawback of this technique is the requirement to first segment characters, with the process relying on manually configured operators, which poses a challenge when dealing with CAPTCHAs that contain overlapping characters. [PERSON] et al. proposed a CRNN based on the Connectionist Temporal Classification (CTC) loss function and an RNN, which improved the detection ability of variable-length characters [19]. More recently, capsule networks have been used due to their capability of preserving detailed information about the input [20]. Nevertheless, this approach is extremely computationally intensive, and later trials indicate that the Attack Success Rate (ASR) is not good for CAPTCHAs with significant levels of noise. [PERSON] et al. introduced a network called PosConv that utilizes the positional information in the character sequence without using an RNN. This network employs a unique padding method and modified convolution to directly incorporate the relative position into the local features of letters [21]. In 2022, [PERSON] and his colleagues employed the Depth First Search (DFS) method to extract characters from CAPTCHAs, called DeCAPTCHA [22]. They then utilized a CNN to recognize these extracted characters. However, the process of segmentation is significantly impacted by the presence of noise and interference, as well as the complexity of the calculations involved. [PERSON] et al. introduced a ConvNet that incorporates a unique group convolution operation across the width of the image [23]. This operation effectively reduces unnecessary calculations, resulting in enhanced performance. In 2023, [PERSON] and his colleagues assessed the weaknesses in the CAPTCHA systems of government websites. The authors provide an innovative neural network structure designed to effectively solve CAPTCHAs by incorporating textual instructions into the image [24]. [PERSON] et al. introduced a multiview deep learning method for cracking CAPTCHAs. This approach utilizes correlational characteristics from many perspectives to enhance the model's ability to generalize and accurately classify [25]. A method was proposed by [PERSON] et al. for recognizing CAPTCHAs that involves making several copies of the original CAPTCHA pictures and producing individual binary images with the precise positions of each set of CAPTCHA letters [26]. [PERSON] et al. developed a technique to bypass CAPTCHAs using a sequential CNN model known as CAP-SECURE, which is employed to assess the flaws and susceptibilities of websites [27]. Deep CAPTCHA is a state-of-the-art model that is used in the field of text-based CAPTCHA recognition, as proposed by [PERSON] et al. in 2020. The model consists of a series of convolutional layers and pooling layers, which are responsible for extracting features from the input, as shown in Figure 1. These features are then fed into FC layers for the classification task. The probabilities of each character in the CAPTCHA are finally outputted by several SoftMax functions individually. However, this model does not adequately suppress noise and interference by filtering, considering the large number of noise spots and interference lines that are specially designed for CAPTCHAs.