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"

50 / 1,401 papers shown

Title
Deep Sparse Band Selection for Hyperspectral Face Recognition Fariborz Taherkhani J. Dawson Nasser M. Nasrabadi CVBM 20 11 0 15 Aug 2019
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once Jiangfan Han Xiaoyi Dong Ruimao Zhang Dongdong Chen Weiming Zhang Nenghai Yu Ping Luo Xiaogang Wang AAML 24 28 0 14 Aug 2019
PCGAN-CHAR: Progressively Trained Classifier Generative Adversarial Networks for Classification of Noisy Handwritten Bangla Characters Qun Liu Edward Collier S. Mukhopadhyay 18 10 0 11 Aug 2019
Deep Learning for Detecting Building Defects Using Convolutional Neural Networks H. Perez J. Tah Amir H. Mosavi 23 194 0 06 Aug 2019
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding Utku Ozbulak Arnout Van Messem W. D. Neve AAML 19 1 0 30 Jul 2019
Towards Adversarially Robust Object Detection Haichao Zhang Jianyu Wang AAML ObjD 25 130 0 24 Jul 2019
ImageNet-trained deep neural network exhibits illusion-like response to the Scintillating Grid Eric Sun Ron Dekel 28 4 0 21 Jul 2019
ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks Xuankang Lin He Zhu R. Samanta Suresh Jagannathan AAML 27 28 0 17 Jul 2019
Natural Adversarial Examples Dan Hendrycks Kevin Zhao Steven Basart Jacob Steinhardt D. Song OODD 109 1,428 0 16 Jul 2019
Modeling User Selection in Quality Diversity Alexander Hagg A. Asteroth Thomas Bäck 35 9 0 16 Jul 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 32 67 0 16 Jul 2019
A Systematic Mapping Study on Testing of Machine Learning Programs S. Sherin Muhammad Uzair Khan Muhammad Zohaib Z. Iqbal 30 13 0 11 Jul 2019
Prior Activation Distribution (PAD): A Versatile Representation to Utilize DNN Hidden Units L. Meegahapola Vengateswaran Subramaniam Lance M. Kaplan Archan Misra 20 2 0 05 Jul 2019
Treant: Training Evasion-Aware Decision Trees Stefano Calzavara Claudio Lucchese Gabriele Tolomei S. Abebe S. Orlando AAML 30 41 0 02 Jul 2019
Evolving Robust Neural Architectures to Defend from Adversarial Attacks Shashank Kotyan Danilo Vasconcellos Vargas OOD AAML 24 36 0 27 Jun 2019
Evolutionary Computation and AI Safety: Research Problems Impeding Routine and Safe Real-world Application of Evolution Joel Lehman 20 7 0 24 Jun 2019
Defending Against Adversarial Examples with K-Nearest Neighbor Chawin Sitawarin David Wagner AAML 11 29 0 23 Jun 2019
Bayesian Modelling in Practice: Using Uncertainty to Improve Trustworthiness in Medical Applications David Ruhe Giovanni Cina Michele Tonutti D. D. Bruin Paul Elbers OOD 12 13 0 20 Jun 2019
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences Shashank Kotyan Danilo Vasconcellos Vargas Moe Matsuki 12 0 0 15 Jun 2019
$Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary$ Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary Shashank Kotyan Danilo Vasconcellos Vargas AAML 17 8 0 14 Jun 2019
Evolutionary Trigger Set Generation for DNN Black-Box Watermarking Jiabao Guo M. Potkonjak AAML WIGM 32 15 0 11 Jun 2019
Proposed Guidelines for the Responsible Use of Explainable Machine Learning Patrick Hall Navdeep Gill N. Schmidt SILM XAI FaML 11 28 0 08 Jun 2019
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks Maksym Andriushchenko Matthias Hein 28 61 0 08 Jun 2019
Outlier Exposure with Confidence Control for Out-of-Distribution Detection Aristotelis-Angelos Papadopoulos Mohammad Reza Rajati Nazim Shaikh Jiamian Wang OODD 19 1 0 08 Jun 2019
Defending Against Universal Attacks Through Selective Feature Regeneration Tejas S. Borkar Felix Heide Lina Karam AAML 23 1 0 08 Jun 2019
Likelihood Ratios for Out-of-Distribution Detection Jie Jessie Ren Peter J. Liu Emily Fertig Jasper Snoek Ryan Poplin M. DePristo Joshua V. Dillon Balaji Lakshminarayanan OODD 50 717 0 07 Jun 2019
Robust Attacks against Multiple Classifiers Juan C. Perdomo Yaron Singer AAML 18 10 0 06 Jun 2019
What do AI algorithms actually learn? - On false structures in deep learning L. Thesing Vegard Antun A. Hansen 11 21 0 04 Jun 2019
Improving Variational Autoencoder with Deep Feature Consistent and Generative Adversarial Training Xianxu Hou Ke Sun Linlin Shen Guoping Qiu GAN DRL 21 53 0 04 Jun 2019
A Case for Backward Compatibility for Human-AI Teams Gagan Bansal Besmira Nushi Ece Kamar Daniel S. Weld Walter S. Lasecki Eric Horvitz 15 8 0 04 Jun 2019
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward A. Qayyum Muhammad Usama Junaid Qadir Ala I. Al-Fuqaha AAML 27 187 0 29 May 2019
Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade T. Lindeberg 27 19 0 29 May 2019
Cross-Domain Transferability of Adversarial Perturbations Muzammal Naseer Salman H. Khan M. H. Khan Fahad Shahbaz Khan Fatih Porikli AAML 33 145 0 28 May 2019
GAT: Generative Adversarial Training for Adversarial Example Detection and Robust Classification Xuwang Yin Soheil Kolouri Gustavo K. Rohde AAML 33 43 0 27 May 2019
AI-GAs: AI-generating algorithms, an alternate paradigm for producing general artificial intelligence Jeff Clune 17 116 0 27 May 2019
Combating Label Noise in Deep Learning Using Abstention S. Thulasidasan Tanmoy Bhattacharya J. Bilmes Gopinath Chennupati J. Mohd-Yusof NoLa 22 178 0 27 May 2019
Adversarial Distillation for Ordered Top-k Attacks Zekun Zhang Tianfu Wu AAML 14 2 0 25 May 2019
Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi S. Maeda Masanori Koyama Takeru Miyato OOD 32 129 0 24 May 2019
Convergence and Margin of Adversarial Training on Separable Data Zachary B. Charles Shashank Rajput S. Wright Dimitris Papailiopoulos AAML 34 16 0 22 May 2019
Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection Jonathan Aigrain Marcin Detyniecki AAML 27 30 0 22 May 2019
A framework for the extraction of Deep Neural Networks by leveraging public data Soham Pal Yash Gupta Aditya Shukla Aditya Kanade S. Shevade V. Ganapathy FedML MLAU MIACV 36 56 0 22 May 2019
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models Shubham Sharma Jette Henderson Joydeep Ghosh 11 87 0 20 May 2019
Testing DNN Image Classifiers for Confusion & Bias Errors Yuchi Tian Ziyuan Zhong Vicente Ordonez Gail E. Kaiser Baishakhi Ray 24 52 0 20 May 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain Lei Bu Yuchao Duan Fu Song Zhe Zhao AAML 37 18 0 19 May 2019
ROSA: Robust Salient Object Detection against Adversarial Attacks Haofeng Li Guanbin Li Yizhou Yu AAML 16 28 0 09 May 2019
Learning with Learned Loss Function: Speech Enhancement with Quality-Net to Improve Perceptual Evaluation of Speech Quality Szu-Wei Fu Chien-Feng Liao Yu Tsao 16 69 0 06 May 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 35 26 0 05 May 2019
Analysis of Confident-Classifiers for Out-of-distribution Detection Sachin Vernekar Ashish Gaurav Taylor Denouden Buu Phan Vahdat Abdelzad Rick Salay Krzysztof Czarnecki OODD 18 18 0 27 Apr 2019
Perceptual Attention-based Predictive Control Keuntaek Lee G. N. An Viacheslav Zakharov Evangelos A. Theodorou 15 19 0 26 Apr 2019
The Scientific Method in the Science of Machine Learning Jessica Zosa Forde Michela Paganini 24 35 0 24 Apr 2019