specific topic. Additionally, the search for “synthetic medical images” is not equivalent to the “medical imaging adversarial examples” search, because the first are mainly used for data augmentation of small datasets, which means that they are not constructed to mislead ML models but the ...
Code Issues Pull requests Raising the Cost of Malicious AI-Powered Image Editing computer-visiondeep-learningrobustnessadversarial-examplesadversarial-attacksdeepfakesstable-diffusion UpdatedFeb 27, 2023 Jupyter Notebook 🗣️ Tool to generate adversarial text examples and test machine learning models agains...
and Llama-2 without having direct access to them. The examples shown here are all actual outputs of these systems. The adversarial prompt can elicit arbitrary harmful behaviors from these models with high probability, demonstrating potentials for misuse. To achieve this, our attack (Greedy Coordinate...
Generating adversarial examples for NLP models [TextAttack Documentation on ReadTheDocs] About•Setup•Usage•Design About TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP. If you're looking for information about TextAttack's menagerie of pre-tra...
In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017) Google Scholar Kos, J., Fischer, I., Song, D.: Adversarial examples for generative models. In: 2018 IEEE Security and Privacy Workshops (SPW), pp. 36–42. IEEE (2018) Google ...
Fig. 1: Examples of adversarial images used as stimuli in past research. The standard procedure for generating adversarial perturbations starts with a pretrained ANN classifier that maps RGB images to a probability distribution over a fixed set of classes25. When presented with an uncorrupted image,...
However, the introduction of such systems has introduced an additional attack vector; the trained models may also be subject to attacks. The act of deploying attacks towards machine learning-based systems is known as Adversarial Machine Learning (AML). The aim is to exploit the weaknesses of the...
Present a likelihood-free method to estimate parameters in implicit models. It is to approximate the result of maximizing the likelihood. The assumptions: the capacity of the model is finite; the number of data examples is finite. The proposed method relies on the following observation: a model...
In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the ...
In this work, we provide a theoretical foundation for crafting transferable adversarial examples to entire hypothesis classes. We introduce Adversarial Example Games (AEG), a framework that models the crafting of adversarial examples as a min-max game between a generator of attacks and a classifier....