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Found 12 papers, 1 papers with code
Adversarial Attack on Deep Learning-Based Splice Localization
Regarding image forensics, researchers have proposed various approaches to detect and/or localize manipulations, such as splices.
Improved Adversarial Robustness by Reducing Open Space Risk via Tent Activations
On the CIFAR-10 dataset, our approach improves the average accuracy against the six white-box adversarial attacks to 73. 5% from 41. 8% achieved by adversarial training via PGD.
Facial Attributes: Accuracy and Adversarial Robustness
Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems.
Adversarial Robustness: Softmax versus Openmax
Deep neural networks (DNNs) provide state-of-the-art results on various tasks and are widely used in real world applications.
Towards Robust Deep Neural Networks with BANG
Machine learning models, including state-of-the-art deep neural networks, are vulnerable to small perturbations that cause unexpected classification errors.
LOTS about Attacking Deep Features
We demonstrate that iterative LOTS is very effective and show that systems utilizing deep features are easier to attack than the end-to-end network.
AFFACT - Alignment-Free Facial Attribute Classification Technique
Using an ensemble of three ResNets, we obtain the new state-of-the-art facial attribute classification error of 8. 00% on the aligned images of the CelebA dataset.
Are Accuracy and Robustness Correlated?
In this paper, we perform experiments on various adversarial example generation approaches with multiple deep convolutional neural networks including Residual Networks, the best performing models on ImageNet Large-Scale Visual Recognition Challenge 2015.
Assessing Threat of Adversarial Examples on Deep Neural Networks
Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network.
Are Facial Attributes Adversarially Robust?
We show that FFA generates more adversarial examples than other related algorithms, and that DCNNs for certain attributes are generally robust to adversarial inputs, while DCNNs for other attributes are not.
Adversarial Diversity and Hard Positive Generation
Finally, we demonstrate on LeNet and GoogLeNet that fine-tuning with a diverse set of hard positives improves the robustness of these networks compared to training with prior methods of generating adversarial images.
A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions
While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution.
