Neural network backdoors Backdoor attacks on neural networks have highlighted critical weaknesses of the black-box nature of neural networks throughout a variety of different tasks and model structures (Chen, Liu, Li, Lu, Song, Gu, Dolan-Gavitt, Garg, Liu, Ma, Aafer, Lee, Zhai, Wang, Zhan...
A black-box model does not lend itself to interpretable and meaningful representations, potentially making the model more susceptible to adversarial attacks36,37, Recently, it has become increasingly clear that deep neural networks (DNNs) have the potential to identify biologically meaningful molecular ...
Simple black-box universal adversarial attacks on medical image classification based on deep neural networks Universal adversarial attacks, which hinder most deep neural network (DNN) tasks using only a small single perturbation called a universal adversarial perturbation (UAP), is a realistic security ...
Recently, deep neural networks (DNNs)21,22 have demonstrated the automatic extraction of black-box features from raw operating data, showing impressive SOH estimation performance. However, experimental collection of the target-labeled data is time-consuming and resource-intensive23, and creating massive...
Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions 来自 NCBI 喜欢 0 阅读量: 123 作者:SG Kim,N Theera-Ampornpunt,CH Fang…摘要: Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent ...
The ‘black box’ problem– a phenomenon in which an AI’s decision-making process remains hidden from users – is a significant challenge for the technology’s application in healthcare. Neural networks, a type of deep learning that mimics the neural networks of the human bra...
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning t... PY Chen,H Zhang,Y ...
propose the DEceit algorithm for constructing effective universal pixel-restricted perturbations using only black-box feedback from the target network. We conduct empirical investigations using the ImageNet validation set on the state-of-the-art deep neural classifiers by varying the number of pixels ...
“black box” models such as deep neural networks have helped researchers derive understanding from complex models52. In particular, IG is a method for assigning sample-specific importance scores for inputs of a model on the output based on the gradients of neurons’ weights across the network....
We used Long Short Term Memory (LSTM) as an exploratory, high capacity, black-box model to predict human decision making in the above task. LSTM is a type of recurrent neural networks that allows modelling temporal dynamic behaviour by incorporating feedback connections in their architecture (Fig...