New defense method inspired by encryption improves visual classification accuracy.
problem Conventional defenses reduce accuracy and are defeated by obfuscated gradients.
method Block-wise pixel shuffling with secret key for training and test images.
result Achieves high accuracy (91.55%) on clean images and (89.66%) on adversarial examples.
This study evaluates adversarial attacks and defenses for chest X-ray disease classification.
problem Vulnerability of deep neural networks to adversarial examples in chest X-ray disease detection.
method Detailed introduction and evaluation of various attack and defense methods.
result Attack and defense methods perform poorly with excessive iterations and large perturbations.
ATHENA builds flexible defenses against adversarial attacks.
problem Extensive research on adversarial attacks is domain-specific and cannot be easily extended.
method Designing an extensible framework based on diverse weak defenses.
result Comprehensive empirical study demonstrates the effectiveness of ATHENA.
Paper analyzes adversarial attacks and defenses using game theory.
problem Unclear conditions for optimal attacks and defenses in adversarial learning.
method Game-theoretic framework with locally linear decision boundary model.
result Fast Gradient Method attack and Randomized Smoothing defense form a Nash Equilibrium.
New defense method protects client data privacy in federated learning.
problem Gradient leakage attacks in federated learning.
method Learning to obscure data to generate synthetic samples.
result Synthetic samples preserve predictive features and protect privacy.
New defense method reduces adversarial attacks on neural networks.
problem Vulnerability of deep neural networks to adversarial attacks.
method Sparse coding frontend trained on clean images.
result Significantly attenuates adversarial attacks before reaching classifier.
Stochastic defense improves natural classifiers against adversarial attacks.
problem Vulnerability of deep networks to adversarial attacks.
method Long-run MCMC sampling with Energy-Based Model for adversarial purification.
result Balancing memoryless and metastable behavior leads to effective purification and robust classification.
Paper presents certified defenses against adversarial patch attacks.
problem Certified defenses against adversarial patch attacks are needed.
method Proposes the first certified defense and faster training methods.
result Demonstrates robustness transfer across different patch shapes.
Deep Partition Aggregation defends against poisoning attacks with provable certificates.
problem Adversarial poisoning attacks corrupt classifier test-time behavior.
method Deep Partition Aggregation (DPA) is an ensemble method using hash partitions and base models trained on these partitions.
result DPA can certify >= 50% of test images against over 500 poison image insertions on MNIST, and nine insertions on CIFAR-10.
Correctly evaluating defenses against adversarial examples has proven to be extremely difficult. Despite the significant amount of recent work attempting to design defenses that withstand adaptive attacks, few have succeeded; most papers that propose defenses are quickly shown to be incorrect. We believe a large contri…
Paper presents a defense framework against adversarial examples.
problem Vulnerability of deep neural networks to adversarial examples.
method Cross-layer strategic ensemble defense with input and output transformations.
result Strategic ensemble defense achieves high defense success rates and robustness.
In recent years, Deep Neural Networks (DNNs) have had a dramatic impact on a variety of problems that were long considered very difficult, e. g., image classification and automatic language translation to name just a few. The accuracy of modern DNNs in classification tasks is remarkable indeed. At the same time, attack…
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. This work provides a solution to hardening DNNs under adversarial attacks through…
In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new fra…
This paper analyzes FL privacy risks and defensive strategies.
problem Privacy leakage in federated learning.
method Literature review of attack methods and defensive strategies.
result No single defensive strategy is sufficient for all attacks.
EBM life cycle project improves MCMC for image generation, defense, and density modeling.
problem Improving MCMC for diverse EBM applications.
method Three novel MCMC initialization methods for negative samples.
result State-of-the-art performance gains across three applications.
Interpreting machine learning models helps understand adversarial attacks and defenses.
problem Understanding model vulnerability to adversarial attacks.
method Model interpretation techniques to explore adversarial attacks and defenses.
result Interpretation methods can be applied to adversarial attacks and defenses.
Deep neural networks are widely used and exhibit excellent performance in many areas. However, they are vulnerable to adversarial attacks that compromise the network at the inference time by applying elaborately designed perturbation to input data. Although several defense methods have been proposed to address specific…
Paper examines test-time smoothing defenses against adversarial examples.
problem Adversarial examples can mislead neural networks, compromising robustness.
method Evaluates various test-time smoothing defenses on ImageNet.
result Non-monotonic relation between attacks and defenses, large variance among samples.
FDA3 defends IIoT applications against adversarial attacks by federating defense knowledge.
problem Adversarial attacks on DNNs in IIoT applications can cause devastating consequences.
method Federated learning approach to aggregate defense knowledge from different sources.
result FDA3 can resist more attacks than existing methods and prevent new attacks.
Unified framework for analyzing model stealing attacks and defenses.
problem Vulnerability of ML applications to model stealing attacks.
method Developed a rigorous threat model and evaluation criteria, proposed methods to quantify attack and defense strategies.
result Demonstrated the importance of attack-specific perturbations for effective defenses.
Despite their accuracy, neural network-based classifiers are still prone to manipulation through adversarial perturbations. Those perturbations are designed to be misclassified by the neural network, while being perceptually identical to some valid input. The vast majority of attack methods rely on white-box conditions…
Automated discovery of adaptive attacks improves adversarial defense evaluation.
problem Challenges in reliably evaluating adversarial defenses.
method Formalizes adaptive attacks as reusable building blocks in a search space for automatic discovery.
result Our tool discovers significantly stronger attacks than AutoAttack, improving adversarial defense evaluation.
New research shows many recent defenses against adversarial examples are ineffective against black-box attacks.
problem The robustness of recent defenses against adversarial examples is insufficient, especially against black-box attacks.
method Evaluation of nine defenses on two black-box adversarial models and six attacks on CIFAR-10 and Fashion-MNIST datasets.
result Most recent defenses provide only marginal improvements in security (<25%) compared to undefended networks. How can we make machine learning provably robust against adversarial examples in a scalable way? Since certified defense methods, which ensure ε-robust, consume huge resources, they can only achieve small degree of robustness in practice. Lipschitz margin training (LMT) is a scalable certified defense, but it can als…
New method defends against patch attacks with high-certainty guarantees.
problem Patch attacks on images, especially physical adversarial attacks.
method Randomized smoothing, exploiting patch constraints.
result Meaningfully large robustness certificates against patch attacks.
Improved defense against data poisoning attacks by aggregating smaller subsets.
problem Mitigating the impact of poisoned data on model robustness.
method Finite Aggregation method that combines duplicates of smaller disjoint subsets for training.
result Consistent improvement in certified robustness bounds, up to 4.77% on GTSRB.
New research evaluates various perturbation methods for improving neural network robustness.
problem Understanding and improving robustness of Convolutional Neural Networks (CNNs) against adversarial attacks.
method Detailed evaluation of five main perturbation-based defenses, comparing random and deterministic approaches.
result Perturbation-based defenses are equivalent in efficacy, and attacks transfer between them.
Deep learning has become the state of the art approach in many machine learning problems such as classification. It has recently been shown that deep learning is highly vulnerable to adversarial perturbations. Taking the camera systems of self-driving cars as an example, small adversarial perturbations can cause the sy…
New method defends against neural backdoors using generative modeling.
problem Neural backdoor attacks pose a significant security threat to deep learning models.
method Proposes max-entropy staircase approximator (MESA) for high-dimensional sampling-free generative modeling of backdoor trigger distributions.
result Demonstrates the effectiveness of MESA in modeling backdoor trigger distributions and robustness of the proposed defense method.
Defense against Wasserstein adversarial attacks using randomized smoothing.
problem Certified robustness against Wasserstein adversarial attacks.
method Randomized smoothing applied to the space of flows between images, bounding Wasserstein distance by L_1 distance.
result Significantly improved accuracy under Wasserstein adversarial attacks compared to unprotected models.
Study on detecting backdoor attacks using adversarial detection methods.
problem Mitigating backdoor attacks in deep neural networks.
method Comprehensive studies on adversarial and backdoor examples, revising adversarial defense methods.
result Adversarial defense methods can be effective in detecting backdoor attacks.
Survey of adversarial examples in visual machine learning models.
problem Vulnerability of machine learning models to adversarial examples.
method Exploration of various adversarial attack and defense methods.
result Understanding of adversarial attack and defense mechanisms.
MadNet uses MAD optimization to enhance deep model robustness against adversarial attacks.
problem Defending deep models against adversarial attacks.
method Inspired by certificate defense, MAD optimization increases separability of class clusters and decreases sensitivity to small distortions.
result MadNet improves adversarial robustness compared to state-of-the-art methods.
Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defen…
SPECTRE defends against backdoor attacks by amplifying corrupted data's spectral signature.
problem Backdoor attacks that change model behavior with specific triggers.
method Robust covariance estimation to amplify spectral signature of poisoned data.
result Clean model is completely removed from backdoor, even in hard-to-detect cases.
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and diff…
There is great potential for damage from adversarial learning (AL) attacks on machine-learning based systems. In this paper, we provide a contemporary survey of AL, focused particularly on defenses against attacks on statistical classifiers. After introducing relevant terminology and the goals and range of possible kno…
New adaptive attacks bypass many defenses to adversarial examples.
problem Adversarial example defenses are not adequately evaluated using adaptive attacks.
method Detailed analysis of thirteen defenses, demonstrating their vulnerabilities to adaptive attacks.
result Adversarial example defenses are more vulnerable to adaptive attacks than previously thought.
Adversarially robust machine learning has received much recent attention. However, prior attacks and defenses for non-parametric classifiers have been developed in an ad-hoc or classifier-specific basis. In this work, we take a holistic look at adversarial examples for non-parametric classifiers, including nearest neig…
Defense against small image patches using occlusions.
problem Vulnerability of deep learning to small adversarial patches.
method Partially occlude image around each patch location.
result Certified security against patch attacks of a certain size.
This paper proposes a framework for certifying neural network defenses against data poisoning attacks.
problem Vulnerability of neural networks to data poisoning attacks.
method Random selection based defenses that average predictions on sub-datasets sampled from the training set.
result The certified radius of bagging derived by the framework is tighter than previous work.
New black-box attack method improves GNN defense without needing training data.
problem Vulnerability of Graph Neural Networks to adversarial attacks.
method Developed a gradient-based black-box attack algorithm, BBGA, which does not require access to training data.
result BBGA achieves stable attack performance without accessing training sets, and is effective against various defenses.
Defense against ASR attacks using dropout uncertainty.
problem Adversarial attacks on ASR systems.
method Dropout uncertainty in neural networks.
result High detection accuracy across various ASR systems and datasets.
Deep learning algorithms and networks are vulnerable to perturbed inputs which is known as the adversarial attack. Many defense methodologies have been investigated to defend against such adversarial attack. In this work, we propose a novel methodology to defend the existing powerful attack model. We for the first time…
Survey of algorithms to correct past mistakes in prediction.
problem Improving prediction accuracy by correcting past errors.
method Defensive Forecasting as a sequential game theory approach to minimize prediction metrics.
result Simple, near-optimal algorithms for various prediction tasks.
Federated learning is vulnerable to backdoor attacks; a new defense method is proposed.
problem Backdoor attacks in federated learning that can misclassify models.
method Adjusting the learning rate based on sign information of agents' updates.
result Our defense significantly reduces or eliminates backdoor attacks in federated learning.
Machine-learning (ML) algorithms or models, especially deep neural networks (DNNs), have shown significant promise in several areas. However, researchers have recently demonstrated that ML algorithms, especially DNNs, are vulnerable to adversarial examples (slightly perturbed samples that cause misclassification). The …