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Found 8 papers, 4 papers with code
Backdoor Attacks on Vision Transformers
Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs.
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years.
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
On the other hand, far away NNs may not be semantically related to the query.
Hidden Trigger Backdoor Attacks
Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time.
Role of Spatial Context in Adversarial Robustness for Object Detection
However, one can show that an adversary can design adversarial patches which do not overlap with any objects of interest in the scene and exploit contextual reasoning to fool standard detectors.
Fooling Network Interpretation in Image Classification
Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms.
Confidence estimation in Deep Neural networks via density modelling
State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise.
Training Sparse Neural Networks
Deep neural networks with lots of parameters are typically used for large-scale computer vision tasks such as image classification.
