Abstract The recent period has witnessed automated crawlers designed to automatically crack passwords, which greatly risks various aspects of our lives. To prevent passwords from being cracked, image verification codes have been implemented to accomplish the human–machine verification. It is important to note, however, that the most widely‐used image verification codes, especially the visual reasoning Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs), are still susceptible to attacks by artificial intelligence. Taking the visual reasoning CAPTCHAs representing the image verification codes, this study introduces an enhanced approach for generating image verification codes and proposes an improved Convolutional Neural Network (CNN)‐based recognition system. After we add a fully connected layer and briefly solve the edge of stability issue, the accuracy of the improved CNN model can smoothly approach 98.40% within 50 epochs on the image verification codes with four digits using a large initial learning rate of 0.01. Compared with the baseline model, it is approximately 37.82% better in accuracy without obvious curve oscillation. The improved CNN model can also smoothly reach the accuracy of 99.00% within 7500 epochs on the image verification codes with six characters, including digits, upper‐case alphabets, lower‐case alphabets, and symbols. A detailed comparison between our proposed approach and the baseline one is presented. The relationship between the time consumption and the length of the seeds is compared theoretically. Subsequently, we figure out the threat assignments on the visual reasoning CAPTCHAs with different lengths based on four machine learning models. Based on the threat assignments, the Kaplan‐Meier (KM) curves are computed.

Uncertainty pervades through the modern robotic autonomy stack, with nearly every component (e.g., sensors, detection, classification, tracking, behavior prediction) producing continuous or discrete probabilistic distributions. Trajectory forecasting, in particular, is surrounded by uncertainty as its inputs are produced by (noisy) upstream perception and its outputs are predictions that are often probabilistic for use in downstream planning. However, most trajectory forecasting methods do not account for upstream uncertainty, instead taking only the most-likely values. As a result, perceptual uncer-tainties are not propagated through forecasting and predictions are frequently overconfident. To address this, we present a novel method for incorporating perceptual state uncertainty in trajectory forecasting, a key component of which is a new statistical distance-based loss function which encourages predicting uncertainties that better match upstream perception. We evaluate our approach both in illustrative simulations and on large-scale, real-world data, demonstrating its efficacy in propagating perceptual state uncertainty through prediction and producing more calibrated predictions.