Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

5 December 2014

Jeff Clune

Papers citing "Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images"

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Title
Connecting Image Denoising and High-Level Vision Tasks via Deep Learning Ding Liu Bihan Wen Jianbo Jiao Xianming Liu Zhangyang Wang Thomas S. Huang 29 144 0 06 Sep 2018
Adversarial Attack Type I: Cheat Classifiers by Significant Changes Sanli Tang Xiaolin Huang Mingjian Chen Chengjin Sun J. Yang AAML 38 2 0 03 Sep 2018
Extreme Value Theory for Open Set Classification -- GPD and GEV Classifiers Edoardo Vignotto Sebastian Engelke 13 16 0 29 Aug 2018
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge Pasquale Minervini Sebastian Riedel AAML NAI GAN 21 118 0 26 Aug 2018
Are You Tampering With My Data? Michele Alberti Vinaychandran Pondenkandath Marcel Würsch Manuel Bouillon Mathias Seuret Rolf Ingold Marcus Liwicki AAML 37 19 0 21 Aug 2018
Out-of-Distribution Detection using Multiple Semantic Label Representations Gabi Shalev Yossi Adi Joseph Keshet OODD 24 85 0 20 Aug 2018
Reinforcement Learning for Autonomous Defence in Software-Defined Networking Yi Han Benjamin I. P. Rubinstein Tamas Abraham T. Alpcan O. Vel S. Erfani David Hubczenko C. Leckie Paul Montague AAML 22 68 0 17 Aug 2018
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding Lea Schonherr Katharina Kohls Steffen Zeiler Thorsten Holz D. Kolossa AAML 33 287 0 16 Aug 2018
Out of the Black Box: Properties of deep neural networks and their applications Nizar Ouarti D. Carmona FAtt AAML 22 3 0 10 Aug 2018
Structured Adversarial Attack: Towards General Implementation and Better Interpretability Kaidi Xu Sijia Liu Pu Zhao Pin-Yu Chen Huan Zhang Quanfu Fan Deniz Erdogmus Yanzhi Wang Xinyu Lin AAML 29 160 0 05 Aug 2018
Traits & Transferability of Adversarial Examples against Instance Segmentation & Object Detection Raghav Gurbaxani Shivank Mishra AAML 13 4 0 04 Aug 2018
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty Savas Ozkan G. Akar UQCV 14 13 0 03 Aug 2018
Generative Adversarial Frontal View to Bird View Synthesis Xinge Zhu Zhichao Yin Jianping Shi Hongsheng Li Dahua Lin GAN 18 52 0 01 Aug 2018
EagleEye: Attack-Agnostic Defense against Adversarial Inputs (Technical Report) Yujie Ji Xinyang Zhang Ting Wang AAML 33 2 0 01 Aug 2018
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Xia Hu FaML 39 1,073 0 31 Jul 2018
Diverse feature visualizations reveal invariances in early layers of deep neural networks Santiago A. Cadena Marissa A. Weis Leon A. Gatys Matthias Bethge Alexander S. Ecker FAtt 11 28 0 27 Jul 2018
Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study Zhenyu Wu Zhangyang Wang Zhaowen Wang Hailin Jin AAML PICV 33 153 0 22 Jul 2018
Recent Advances in Deep Learning: An Overview Matiur Rahman Minar Jibon Naher VLM 29 116 0 21 Jul 2018
Simultaneous Adversarial Training - Learn from Others Mistakes Zukang Liao AAML GAN 22 4 0 21 Jul 2018
Physical Adversarial Examples for Object Detectors Kevin Eykholt Ivan Evtimov Earlence Fernandes Bo Li Amir Rahmati Florian Tramèr Atul Prakash Tadayoshi Kohno D. Song AAML 24 466 0 20 Jul 2018
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning P. Gay Stuart James Alessio Del Bue OCL 55 31 0 16 Jul 2018
Manifold Adversarial Learning Shufei Zhang Kaizhu Huang Jianke Zhu Yang Liu OOD AAML 29 5 0 16 Jul 2018
Neural Networks Regularization Through Representation Learning Soufiane Belharbi OOD SSL 30 2 0 13 Jul 2018
A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising Jun Xu Lei Zhang David C. Zhang 27 249 0 11 Jul 2018
With Friends Like These, Who Needs Adversaries? Saumya Jetley Nicholas A. Lord Philip Torr AAML 21 70 0 11 Jul 2018
Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure Hamed Hakkak OffRL AI4CE 15 1 0 08 Jul 2018
Adversarial Examples in Deep Learning: Characterization and Divergence Wenqi Wei Ling Liu Margaret Loper Stacey Truex Lei Yu Mehmet Emre Gursoy Yanzhao Wu AAML SILM 36 18 0 29 Jun 2018
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples Freda Shi Jiayuan Mao Tete Xiao Yuning Jiang Jian Sun ObjD 25 51 0 27 Jun 2018
DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation Hui Xu Yuxin Su Zirui Zhao Yangfan Zhou Michael R. Lyu Irwin King FedML 13 26 0 27 Jun 2018
A Theory of Diagnostic Interpretation in Supervised Classification Anirban Mukhopadhyay FaML FAtt 8 1 0 26 Jun 2018
Exploring Adversarial Examples: Patterns of One-Pixel Attacks David Kügler Alexander Distergoft Arjan Kuijper Anirban Mukhopadhyay AAML MedIm 25 2 0 25 Jun 2018
SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structural Similarity A. Abobakr M. Hossny S. Nahavandi 24 4 0 24 Jun 2018
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention Phongtharin Vinayavekhin Subhajit Chaudhury Asim Munawar Don Joven Agravante Giovanni De Magistris Daiki Kimura Ryuki Tachibana AI4TS 21 24 0 22 Jun 2018
Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters Manohar Karki Qun Liu Robert DiBiano Saikat Basu S. Mukhopadhyay 17 11 0 21 Jun 2018
Como funciona o Deep Learning M. Ponti G. B. P. D. Costa 37 13 0 20 Jun 2018
Data-Efficient Design Exploration through Surrogate-Assisted Illumination Adam Gaier A. Asteroth Jean-Baptiste Mouret 25 79 0 15 Jun 2018
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data Jacson Rodrigues Correia-Silva Rodrigo Berriel C. Badue Alberto F. de Souza Thiago Oliveira-Santos MLAU 22 174 0 14 Jun 2018
Sufficient Conditions for Idealised Models to Have No Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks Y. Gal Lewis Smith AAML BDL 52 34 0 02 Jun 2018
Interpreting Deep Learning: The Machine Learning Rorschach Test? Adam S. Charles AAML HAI AI4CE 27 9 0 01 Jun 2018
Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders Partha Ghosh Arpan Losalka Michael J. Black AAML 21 77 0 31 May 2018
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 40 1,842 0 31 May 2018
Multi-Layered Gradient Boosting Decision Trees Ji Feng Yang Yu Zhi-Hua Zhou AI4CE 30 120 0 31 May 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 39 1,757 0 30 May 2018
Robustifying Models Against Adversarial Attacks by Langevin Dynamics Vignesh Srinivasan Arturo Marbán K. Müller Wojciech Samek Shinichi Nakajima AAML 25 9 0 30 May 2018
To Trust Or Not To Trust A Classifier Heinrich Jiang Been Kim Melody Y. Guan Maya R. Gupta UQCV 32 464 0 30 May 2018
Towards the first adversarially robust neural network model on MNIST Lukas Schott Jonas Rauber Matthias Bethge Wieland Brendel AAML OOD 14 369 0 23 May 2018
Semantic Network Interpretation Pei Guo Ryan Farrell MILM FAtt 22 0 0 23 May 2018
Classification Uncertainty of Deep Neural Networks Based on Gradient Information Philipp Oberdiek Matthias Rottmann Hanno Gottschalk UQCV 31 64 0 22 May 2018
Learning long-range spatial dependencies with horizontal gated-recurrent units Drew Linsley Junkyung Kim Vijay Veerabadran Thomas Serre 19 158 0 21 May 2018