AI Security Bot
@AiSecurityBot
I tweet about #AISecurity. Bot by @RandomAdversary
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Building safe artificial intelligence: specification, robustness, and assurance medium.com/@deepmindsafet…
Our work on adversarial attacks against automated speech recognition systems via psychoacoustic hiding is now available: adversarial-attacks.net - listen to the audio samples to hear the attack in practice
The Current State of Adversarial Machine Learning by @infosecanon. Presented on @bsideslv 2018 youtu.be/TPpxkVCbL4c
Are adversarial examples inevitable?. Ali Shafahi, W. Ronny Huang, Christoph Studer, Soheil Feizi, and Tom Goldstein arxiv.org/abs/1809.02104
Non-Negative Networks Against Adversarial Attacks Adversarial attacks against Neural Networks are a problem of considerable importance, for which effective defenses are not yet readily available. We make progress toward this problem by showin... arxiv.org/abs/1806.06108…
Congratulations to @gamaleldinfe , @sh_reya , @thisismyhat , @NicolasPapernot , @alexey2004 and @jaschasd ! "Adversarial Examples that Fool both Computer Vision and Time-Limited Humans" arxiv.org/abs/1802.08195 was accepted to NIPS 2018.
Second-Order Adversarial Attack and Certifiable Robustness. Bai Li, Changyou Chen, Wenlin Wang, and Lawrence Carin arxiv.org/abs/1809.03113
Adversarial examples from computational constraints Why are classifiers in high dimension vulnerable to "adversarial" perturbations? We show that it is likely not due to information theoretic limitations, but rather it could be due to computa... arxiv.org/abs/1805.10204…
Detecting Homoglyph Attacks with a Siamese Neural Network A homoglyph (name spoofing) attack is a common technique used by adversaries to obfuscate file and domain names. This technique creates process or domain names that are visually simila... arxiv.org/abs/1805.09738…
Reinforcement Learning under Threats. Víctor Gallego, Roi Naveiro, and David Ríos Insua arxiv.org/abs/1809.01560
The Effects of JPEG and JPEG2000 Compression on Attacks using Adversarial Examples Adversarial examples are known to have a negative effect on the performance of classifiers which have otherwise good performance on undisturbed images. These e... arxiv.org/abs/1803.10418…
Random depthwise signed convolutional neural networks Random weights in convolutional neural networks have shown promising results in previous studies yet remain below par compared to trained networks on image benchmarks. We explore depthwise... arxiv.org/abs/1806.05789…
The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure. Saeed Mahloujifar, Dimitrios I. Diochnos, and Mohammad Mahmoody arxiv.org/abs/1809.03063
Adversarial Attack Type I: Generating False Positives. Sanli Tang, Xiaolin Huang, Mingjian Chen, and Jie Yang arxiv.org/abs/1809.00594
Adversarial Reprogramming of Sequence Classification Neural Networks. Paarth Neekhara, Shehzeen Hussain, Shlomo Dubnov, and Farinaz Koushanfar arxiv.org/abs/1809.01829
"Making Machine Learning Robust Against Adversarial Inputs," by @goodfellow_ian et al., looks at protecting #MachineLearning systems against adversarial inputs. #AI bit.ly/2ttGVvM
Gradient Band-based Adversarial Training for Generalized Attack Immunity of A3C Path Finding As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of c... arxiv.org/abs/1807.06752…
Unravelling Robustness of Deep Learning based Face Recognition Against Adversarial Attacks Deep neural network (DNN) architecture based models have high expressive power and learning capacity. However, they are essentially a black box method ... arxiv.org/abs/1803.00401…
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