20 papers with code • 5 benchmarks • 4 datasets
Traffic sign recognition is the task of recognising traffic signs in an image or video.
To alleviate these problems, this paper proposes the targeted attention attack (TAA) method for real world road sign attack.
Research into adversarial examples (AE) has developed rapidly, yet static adversarial patches are still the main technique for conducting attacks in the real world, despite being obvious, semi-permanent and unmodifiable once deployed.
To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution.
Ranked #1 on Domain Adaptation on SYNSIG-to-GTSRB
We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics.
Under the standard paradigm of network fine-tuning, an entirely new CNN is learned per task, and the final network size is independent of task complexity.
In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs).
Attackers' optimization algorithms gravitate towards trapdoors, leading them to produce attacks similar to trapdoors in the feature space.
Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory.
Recognizing Traffic Signs using intelligent systems can drastically reduce the number of accidents happening world-wide.
When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently.