Code Issues Pull requests Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with...
For example, the adversarial robustness ofΔCLIP surpasses that of the previous best models on ImageNet-1k by ~20%. Paper Add Code Towards a constructive framework for control theory no code yet •4 Jan 2025 Such observations indicate that computational uncertainty should indeed be addressed expl...
Please cite our paper if you use the code in this repo.@inproceedings{jiang-etal-2023-lion, title = "Lion: Adversarial Distillation of Proprietary Large Language Models", author = "Jiang, Yuxin and Chan, Chunkit and Chen, Mingyang and Wang, Wei", editor = "Bouamor, Houda and Pino, ...
Aligned LLMs are not adversarially aligned. Our attack constructs a single adversarial prompt that consistently circumvents the alignment of state-of-the-art commercial models including ChatGPT, Claude, Bard, and Llama-2 without having direct access to them. The examples shown here are all actual ...
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 many cases, a wide variety of models with different architectures trained on different subsets of the training data misclassify the same adversarial example. This suggests thatadversarial examples expose fundamental blind spots in our training algorithms. Onsome datasets, such as ImageNet (Deng et...
by the generator of the adversarial network (which is the encoder of the autoencoder) instead of a KL divergence for it to learn how to produce samples according to the distribution $p(z)$. This modification allows us to use a broader set of distributions as priors for the latent code. ...
Deep neural networks (DNNs) are vulnerable to adversarial examples that are similar to original samples but contain the perturbations intentionally crafted by adversaries. Many efficient and typical attacks are based on the fast gradient sign method and usually against models by adding invariant perturbat...
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...
as illustrated in Fig.1a. This adversarial effect often transfers to ANN models trained on a different data set25, with a different algorithm28, or even to machine learning algorithms with fundamentally different architectures25(e.g., adversarial examples designed to fool a convolution neural network...