# A Survey on Adversarial Perturbations and Attacks on CAPTCHAs
## 3 Deep Learning Attacks against CAPTCHAs
### Deep Learning Attack
Based on these results, this study identified some efficient techniques to enhance the robustness of the text-based CAPTCHAs. A dynamic approach is proposed in [30] that predicts the text-based CAPTCHAs. In particular, this approach firstly uses a pre-processing step through several techniques like Erosion, Dilation, and Binarization in order to remove the noise from the CAPTCHA. This CAPTCHA is then fed to the CNN that generates a feature vector. This feature vector is then passed to the long short-term memory (LSTM) which generates a sequence of characters that reflect the outcome to the users. Additionally, in [31], an efficient CNN model is introduced that uses attached binary images to recognize CAPTCHAs without the segmentation of CAPTCHAs into individual characters. The results revealed the strength of the introduced model in recognizing CAPTCHAs' characters. Additionally, a study in [12] proposed an efficient CAPTCHA solver that periodically retrains the solver model when its accuracy drops using an incremental learning. This proposed solver requires a small amount of data while achieving a high accuracy. The results of evaluating the proposed solver demonstrated that the existing defense methods based on a text-based CAPTCHA scheme and an image-based CAPTCHA scheme can be bypassed. A transfer learning-based approach is proposed in [32] in which the attack complexity and the cost of labeling samples are reduced by pre-training the model. This model randomly produced samples and fine-tuning the pre-trained model with a small number of real-world samples. Furthermore, a GAN is applied to refine the samples sequentially. The results of evaluating this approach showed that the cost of data preparation is reduced while preserving the model's attack accuracy. For enhancing the accuracy of CAPTCHA attacks, a simple preprocessing approach is introduced in [33]. This approach includes a data selector, which automatically filters out a training data set with training significance, and a data augmenter, which applies four different image noises to generate different CAPTCHA images. The results showed that the attack accuracy rate is improved after applying this approach. In addition, the brute-force attack with transfer learning is combined in [34] for breaking the text-based CAPTCHAs. This achieves 80% classification accuracy for a five-digit text-based CAPTCHA scheme. An efficient end-to-end attack method based on cycle-consistent generative adversarial networks (Cycle-GANs) is proposed in [9]. This method focuses on reducing the cost of data labeling. The results demonstrated efficiently the performance of this method in terms of breaking CAPTCHAs. A recent study in [35] proposed a fast CAPTCHA solver based on GANs to simplify the CAPTCHA images before segmenting and recognizing characters. This effectively breaks the text-based CAPTCHAs with complex security features by a small amount of labeled data. Results showed that the proposed solver achieved a high success rate of over 96% character accuracy. A study in [36] analyzed the security level of exist audio-based CAPTCHA schemes against such attacks using ML and DL models. The experimental results reveal that audio-based CAPTCHAs that had no or medium background noise could be broken with nearly 99% to 100% accuracy, whereas the attack accuracy is decreased to 85% with high background noise. The aforementioned studies are summarized in Table 1.  
In the extant literature, the minimal security level standard for designing a CAPTCHA is varied. That is, the requirements for the possibility of an automatic bypassing of the CAPTCHA system [i.e., the false positive rate (FPR)] range from 0.6% to approximately 5% [3]. This section obviously demonstrates the impact of DL for breaking CAPTCHAs with high accuracy. This is accomplished by applying different DL techniques from 2014 to the present date. Although most of the works are from 2020, they may increase in number in the coming years, resulting in probably more robust CAPTCHA schemes.