Journal ArticleDOI
Fast-UAP: An algorithm for expediting universal adversarial perturbation generation using the orientations of perturbation vectors
Jiazhu Dai,Le Shu +1 more
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TLDR
An optimized algorithm to enhance the performance of generating universal perturbations based on the orientations of perturbation vectors is proposed, which shows that compared with UAP, the ones generated using the proposed algorithm achieved an average fooling-rate increment of 9 % in white-box and black-box attacks.About:
This article is published in Neurocomputing.The article was published on 2021-01-21. It has received 4 citations till now.read more
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T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
Ahmadreza Azizi,Ibrahim Asadullah Tahmid,Asim Waheed,Neal Mangaokar,Jiameng Pu,Mobin Javed,Chandan K. Reddy,Bimal Viswanath +7 more
TL;DR: T-Miner is presented -- a defense framework for Trojan attacks on DNN-based text classifiers that employs a sequence-to-sequence (seq-2-seq) generative model that probes the suspicious classifier and learns to produce text sequences that are likely to contain the Trojan trigger.
Journal ArticleDOI
Generating Natural Adversarial Examples with Universal Perturbations for Text Classification
TL;DR: The authors proposed a framework for generating natural adversarial examples with an adversarially regularized autoencoder (ARAE) model and an inverter model, which maps discrete text into the continuous space, gets the conversion of adversarial samples by adding universal adversarial perturbations in the continuous spaces, and then generates natural adversary examples.
Journal ArticleDOI
Generating natural adversarial examples with universal perturbations for text classification
TL;DR: This paper proposed a framework for generating natural adversarial examples with an adversarially regularized autoencoder (ARAE) model and an inverter model, which maps discrete text into the continuous space, gets the conversion of adversarial samples by adding universal adversarial perturbations in the continuous spaces, and then generates natural adversary examples.
Journal ArticleDOI
TextGuise: Adaptive adversarial example attacks on text classification model
TL;DR: Zhang et al. as discussed by the authors proposed a new adaptive black-box text adversarial example generation scheme, TextGuise, which can automatically select replacement keywords and replacement strategies that efficiently generate adversarial examples with good readability.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
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Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.