Existing research on training-time attacks for deep neural networks (DNNs), such as backdoors, largely assume that models are static once trained, and hidden backdoors trained into models remain active indefinitely.
The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph.
Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive.
However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries.
In particular, query-based black-box attacks do not require knowledge of the deep learning model, but can compute adversarial examples over the network by submitting queries and inspecting returns.
In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.
We empirically show that our proposed watermarks achieve piracy resistance and other watermark properties, over a wide range of tasks and models.
Recent work has proposed the concept of backdoor attacks on deep neural networks (DNNs), where misbehaviors are hidden inside "normal" models, only to be triggered by very specific inputs.
As one of the most popular research fields in machine learning, the research on imbalanced dataset receives more and more attentions in recent years.