It has been shown that adversaries can craft example inputs to neural networks which are similar to legitimate inputs but have been created to purposely cause the neural network to misclassify the input. These adversarial examples are crafted, for example, by calculating gradients of a carefully defined loss function w…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,982 papers · 148 categories
Trend · papers per month
48 results for “defensive techniques”
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.
The paper evaluates machine learning cyber defenses using log data against adversarial attacks.
problem Evaluating the robustness of machine learning cyber defenses against adversarial attacks.
method Developed a testing framework using deep reinforcement learning and adversarial natural language processing.
result Higher dropout levels increase robustness, with 90% dropout probability showing the highest robustness.
Machine learning is vulnerable to adversarial examples: inputs carefully modified to force misclassification. Designing defenses against such inputs remains largely an open problem. In this work, we revisit defensive distillation---which is one of the mechanisms proposed to mitigate adversarial examples---to address it…
Backdoor attacks are possible in feature-partitioned collaborative learning, even without labels.
problem Backdoor attacks in feature-partitioned collaborative learning.
method Demonstrated the feasibility of backdoor attacks and introduced defense techniques.
result Backdoor attacks can be blocked without significantly affecting main task accuracy.
Neural networks are known to be vulnerable to adversarial examples. In this note, we evaluate the two white-box defenses that appeared at CVPR 2018 and find they are ineffective: when applying existing techniques, we can reduce the accuracy of the defended models to 0%.
Study on adversarial examples and defenses for malware classification.
problem Vulnerability of neural networks to adversarial examples in malware classification.
method Analysis of different approaches for crafting adversarial examples and defense techniques in malware domain.
result Comparison of effectiveness of different approaches on multiple datasets.
ME-Net defends neural nets against adversarial attacks by reconstructing images.
problem Adversarial attacks on deep neural networks.
method ME-Net uses matrix estimation to preprocess images, destroying adversarial noise while preserving global structure.
result ME-Net consistently outperforms state-of-the-art defenses on various benchmarks.
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.
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.
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.
Graph deep learning models are vulnerable to adversarial attacks, which this paper addresses.
problem Adversarial attacks on graph data models, especially their discreteness and imperceptible perturbations.
method Proposed both attack and defense techniques, including integrated gradients for attacks and graph inspection for defenses.
result Demonstrated the effectiveness of proposed methods through experiments on various datasets.
This study improves scalability of randomized smoothing for certifying classifier robustness.
problem Certifying machine learning classifiers against adversarial attacks is challenging and scalable solutions are needed.
method The study reviews and explores randomized smoothing and its derivatives, focusing on scalability.
result The study provides theoretical guarantees and discusses scalability challenges of randomized smoothing.
Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force a deep neural network (DNN) to provide adversary-se…
Solving inverse problems continues to be a central challenge in computer vision. Existing techniques either explicitly construct an inverse mapping using prior knowledge about the corruption, or learn the inverse directly using a large collection of examples. However, in practice, the nature of corruption may be unknow…
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learningcs.CR
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…
Paper proposes a new black-box attack method for deep neural networks.
problem Understanding and defending against adversarial attacks on deep neural networks.
method The algorithm finds a probability distribution over a region around the input, making a sample likely an adversarial example.
result The method outperforms state-of-the-art black-box or white-box attack methods for most test cases.
Defense against DL-based lithographic hotspot detectors backdooring attacks reduces success rate from 84% to ~0%
problem DL-based lithographic hotspot detectors are vulnerable to backdoor attacks that can misclassify hotspots.
method Training data augmentation to eliminate intentional biases introduced during training.
result Significant reduction in attack success rate (from 84% to ~0%) using the proposed defense.
TensorShield defends images from adversarial attacks using tensor decomposition.
problem Adversarial attacks on images can fool deep neural networks.
method Tensor decomposition to find low-rank approximations of images, reducing high-frequency perturbations.
result TensorShield outperforms existing methods like SLQ by 14% against FGSM attacks.
Deep RL enhances cyber security through adaptive, responsive, and scalable defenses.
problem Complex, dynamic cyber attacks require adaptive and scalable security solutions.
method Incorporates deep learning into traditional RL for cyber defense.
result Demonstrates the potential of DRL in solving high-dimensional cyber defense problems.
Despite being popularly used in many applications, neural network models have been found to be vulnerable to adversarial examples, i.e., carefully crafted examples aiming to mislead machine learning models. Adversarial examples can pose potential risks on safety and security critical applications. However, existing def…
In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting - an important open scientific question (Athalye et …
New flaw found in SAP defense, reducing its effectiveness to 0.1%.
problem Weakness in Stochastic Activation Pruning defense against adversarial attacks.
method Re-examined the implementation of SAP and introduced a new BPDA attack.
result SAP's effectiveness reduced to 0.1% when properly applied.
DAD++ improves test-time adversarial defense without access to training data.
problem Improving adversarial robustness of pre-trained models without access to training data.
method Test-time Data-free Adversarial Defense (DAD++) with detection and correction schemes.
result DAD++ significantly improves adversarial robustness with minimal clean accuracy drop.
In recent years, deep neural networks have demonstrated outstanding performance in many machine learning tasks. However, researchers have discovered that these state-of-the-art models are vulnerable to adversarial examples: legitimate examples added by small perturbations which are unnoticeable to human eyes. Adversari…
Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quantization-based defense mechanisms, Constant Quantization (CQ) and Trainable Quantization (TQ), to increase the robustness of CNNs against ad…
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.
SingleProp speeds up robust neural network training with minimal certification.
problem Efficiently defending neural networks against adversarial attacks with certified guarantees.
method SingleProp regularizer that requires only one forward pass per training iteration.
result Comparable certified accuracy to state-of-the-art defenses, but significantly faster training.
Recent studies have revealed the vulnerability of deep neural networks: A small adversarial perturbation that is imperceptible to human can easily make a well-trained deep neural network misclassify. This makes it unsafe to apply neural networks in security-critical applications. In this paper, we propose a new defense…
Enhances single-step adversarial training to defend against iterative adversarial examples.
problem Defending against iterative adversarial examples in neural networks.
method Identified and leveraged empirical properties of Iter-Adv to improve Single-Adv.
result Enhanced Single-Adv to defend against iterative adversarial examples with improved accuracy and reduced training cost.
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.
Automates perturbation analysis for neural networks, enabling certified robustness on complex architectures.
problem Limited applicability of existing perturbation analysis methods to complex neural network architectures.
method Developed an automatic framework to generalize LiRPA algorithms to any neural network structure, enabling loss fusion and state-of-the-art certified defense results.
result Demonstrated LiRPA based certified defense on Tiny ImageNet and Downscaled ImageNet.
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 () compared to undefended networks.
New attacks and defenses for GNNs on large graphs.
problem Vulnerability of GNNs to adversarial attacks on large graphs.
method Proposed two sparsity-aware first-order optimization attacks and a robust aggregation function.
result Attacks can double in strength, and defenses are effective at all scales.
Study defenses against data poisoning attacks in online learning.
problem Data poisoning attacks on machine learning models.
method Rigorous study of four standard defenses in both a powerful and a realistic threat model.
result The effectiveness of defenses depends on the ease of the learning problem.
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.
Topology-aware generative models improve manifold-based defenses against adversarial examples.
problem Adversarial examples compromise the reliability of ML models, especially DNNs.
method Investigate if generative models used in manifold-based defenses need to be topology-aware.
result Topology-aware generative models enhance the robustness of manifold-based defenses.
New defense against DNN model stealing attacks by poisoning predictions.
problem Stealing DNN models through black-box access attacks.
method Active perturbation of predictions to mislead attacker's training.
result Effective against a wide range of DNN stealing attacks, reducing attacker's success rate significantly.
New method certifies joint adversarial robustness of model ensembles.
problem Ensuring robustness of model ensembles against adversarial attacks.
method Proposes a novel technique to certify joint robustness, building on prior work on single-model robustness certification.
result Demonstrates the effectiveness of certifying joint robustness of ensembles, improving understanding of ensemble defenses.
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.
In recent years, defending adversarial perturbations to natural examples in order to build robust machine learning models trained by deep neural networks (DNNs) has become an emerging research field in the conjunction of deep learning and security. In particular, MagNet consisting of an adversary detector and a data re…
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.
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.
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.
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.
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 surveys black-box attacks on computer vision models.
problem Real-life adversarial attacks on deep learning models.
method Comprehensive comparative study of attacks and defenses.
result Significant amount of research on reliability of models.