Further, we demonstrate that the compact structure and corresponding initialization from the Lottery Ticket Hypothesis can also help in data-free training.
State-of-the-art adversarial attacks on neural networks use expensive iterative methods and numerous random restarts from different initial points.
Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization.
Our goal is to raise awareness of the threats posed by adversarial examples in this space, and to highlight the importance of hardening copyright detection systems to attacks.
By training classifiers on top of these feature extractors, we produce new models that inherit the robustness of their parent networks.
Clean-label poisoning attacks inject innocuous looking (and "correctly" labeled) poison images into training data, causing a model to misclassify a targeted image after being trained on this data.
Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks.
Standard adversarial attacks change the predicted class label of a selected image by adding specially tailored small perturbations to its pixels.
Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples.
The proposed attacks use "clean-labels"; they don't require the attacker to have any control over the labeling of training data.
Through importing the generated trips of the routing problems into the bus scheduling (blocking) problem, it is shown that the proposed model uses up to 13% fewer buses than the common traditional routing models.
We also define two indices based on stations' shortages and surpluses that reflect the degree of balancing aid a station needs.