Low frequency information in seismic data can improve seismic resolution and imaging accuracy, enhance the quality of inversion, and play an essential role in imaging algorithms such as full-waveform inversion. Sufficiently low frequency data can avoid the cycle skipping phenomenon during full-waveform inversion. During seismic data processing, the protection and reconstruction for low frequency information are therefore of great importance. In this paper, we systematically investigate the extrapolation of pre-stack viscoacoustic seismic low frequency data using a dense convolutional network to effectively establish the nonlinear relationship between high and low frequency data, and realize the extrapolation and reconstruction of viscoacoustic 0-5 Hz low frequency data using 5-30 Hz high-frequency component. And the generalizability of the method for different influencing factors such as wavelets, noise, and models is analyzed using Marmousi2 velocity model forward data. It is demonstrated that the method has high robustness and can be applied to different situations, and the accuracy is higher than that of the traditional convolutional neural networks method. The feasibility of the low frequency extrapolation method based on dense convolutional network is also verified by synthetic data, physical experiment simulation data, and field data testing, and superior to the traditional convolutional neural networks method.

Text‐based completely automated public turing tests to tell computers and humans apart (CAPTCHAs) have been widely deployed across the Internet to defend against undesirable or malicious bot programmes. In this study, the authors provide a systematic analysis of text‐based CAPTCHAs and innovatively improve their earlier attack on hollow CAPTCHAs to expand applicability to attack all the text CAPTCHAs. With this improved attack, they have successfully broken the CAPTCHA schemes adopted by 19 out of the top 20 web sites in Alexa including two versions of the famous ReCAPTCHA. With success rates ranging from 12 to 88.8% (note that the success rate for Yandex CAPTCHA is 0%), they demonstrate the effectiveness of their attack method. It is not only applicable to hollow CAPTCHAs, but also to non‐hollow ones. As their attack casts serious doubt on the viability of current designs, they offer lessons and guidelines for designing better text‐based CAPTCHAs.

CAPTCHA is now a standard security technology for differentiating between computers and humans, and the most widely deployed schemes are text-based. While many text schemes have been broken, hollow CAPTCHAs have emerged as one of the latest designs, and they have been deployed by major companies such as Yahoo!, Tencent, Sina, China Mobile and Baidu. A main feature of such schemes is to use contour lines to form connected hollow characters with the aim of improving security and usability simultaneously, as it is hard for standard techniques to segment and recognize such connected characters, which are however easy to human eyes. In this paper, we provide the first analysis of hollow CAPTCHAs' robustness. We show that with a simple but novel attack, we can successfully break a whole family of hollow CAPTCHAs, including those deployed by all the major companies. While our attack casts serious doubt on the viability of current designs, we offer lessons and guidelines for designing better hollow CAPTCHAs.