Cleaner Adversarial CAPTCHAs: Intelligent Targets and Precise Noise for Usable Security

Traditional CAPTCHAs are increasingly vulnerable to deep learning-based solvers that decode text and images with high accuracy. In this work, we propose methods to strengthen adversarial CAPTCHAs without compromising human usability. First, we introduce a Precise Gradient Method (PGM) that preserves gradient magnitude (rather than discarding it via a sign operator), producing adversarial perturbations with significantly lower perceptual noise. Second, we develop intelligent target class selection, using either dataset-level confusion structure (Class Relations Network) or image-specific softmax probabilities (Distance-Based Target), to steer adversarial perturbations more efficiently. Across multiple modern architectures (MobileNets, EfficientNets, ResNet, and Vision Transformer), our framework achieves faster convergence (fewer iterations), reduced visual distortion, and notably greater robustness under iterative adversarial retraining. Experiments show that our methods consistently reduce iteration counts and perceptual distortion while significantly increasing the difficulty for automated attacks. Our results offer a practical, scalable path toward the next generation of CAPTCHA systems and contribute new insights to the adversarial machine learning landscape focused on security and usability.