Paper tackles fooling deep networks with minimal perturbations.
problem Easily fooling deep neural networks with high confidence predictions.
method Uses integrated adaptive gradients to generate minimal adversarial perturbations.
result Achieves minimal adversarial perturbations for fooling deep networks.
Unified framework resists adversarial and fooling samples.
problem Vulnerability of deep neural networks to adversarial attacks.
method Gaussian mixture variational autoencoder with selective classification.
result Selective classification rejects adversarial samples.
New rules found to fool deep neural networks in text classification.
problem Vulnerabilities of deep neural networks in text classification.
method Coevolutionary optimization algorithm to create imperceptible adversarial samples.
result Universal rules for fooling deep neural networks in text classification exist and are sample and method agnostic.
Adversarial manipulation fools neural network interpretations.
problem Ensuring interpretation methods are robust to model manipulations.
method Integrating manipulation into fine-tuning objective function.
result State-of-the-art interpreters can be fooled with minimal accuracy loss.
Generative model creates universal perturbations to fool classifiers.
problem Vulnerability of neural networks to adversarial examples.
method Generative adversarial network approach to create universal perturbations.
result Generative model improves on existing universal adversarial attacks.
Adversarial attacks on spectrograms can fool audio classifiers trained on waveforms.
problem Susceptibility of audio classifiers to adversarial attacks on spectrograms.
method Applying adversarial attacks to spectrograms and reconstructing audio waveforms.
result Perturbed spectrograms can fool 2D CNNs and 1D CNNs trained on audio waveforms.
Generative models create image perturbations to fool AI models.
problem Creating adversarial examples that fool pre-trained models.
method Trainable deep neural networks for image perturbation generation.
result High fooling rates with small perturbation norms, faster than current methods.
Paper shows adversarial training can be fooled by new type of noise.
problem Adversarial training can be fooled by new types of noise.
method Designing ADVIN, a new type of inducing noise.
result ADVIN can degrade adversarial training robustness by 99.9%.
Universal audio perturbations fool multiple classification models.
problem Creating audio adversarial perturbations that work across different models.
method Two methods: greedy iterative approach and novel penalty formulation.
result The penalty method produces more successful attacks with limited training data.
Adversarial perturbations fool deepfake detectors with high accuracy.
problem Improving deepfake detection accuracy against adversarial attacks.
method Used adversarial perturbations and two defenses: Lipschitz regularization and Deep Image Prior (DIP).
result Deepfake detectors achieved 27% accuracy on perturbed images, compared to 95% on unperturbed.
Deep learning models are vulnerable to adversarial examples that can fool them.
problem Vulnerability of deep learning models to adversarial examples in safety-critical environments.
method Review and taxonomy of methods for generating adversarial examples, countermeasures, and challenges.
result Deep learning models are vulnerable to adversarial examples that can fool them.
Can we manipulate multiple deep neural networks simultaneously?
problem Selective fooling of multiple machine learning systems.
method Formulated as a novel optimization problem.
result It is easy to selectively manipulate multiple MNIST classifiers simultaneously.
LSDAT reduces query efficiency for decision-based adversarial attacks.
problem Improving query efficiency for decision-based adversarial attacks.
method Low-rank and sparse decomposition (LSD) to craft perturbations.
result LSDAT achieves superior fooling rates with fewer queries.
This paper generates natural-looking perturbations to fool classifiers.
problem Generating adversarial examples that mimic natural objects or signals.
method Employing generative adversarial networks and optimization algorithms.
result The approach can fool classification models in both image and audio domains.
Adversarial tweets can fool stock prediction models, causing financial loss.
problem Vulnerability of stock prediction models to adversarial attacks on social media.
method Solving combinatorial optimization problems with semantic and budget constraints to generate adversarial tweets.
result Adversarial tweets can fool stock prediction models and cause significant financial loss.
Paper shows how to fool mammogram classifiers with adversarial attacks.
problem Vulnerability of mammographic image classifiers to adversarial attacks.
method Trained model on mamographic images, generated adversarial samples, analyzed similarity.
result Demonstrated successful adversarial attacks on mammographic image classifier.
Researchers create adversarial examples to deceive iris recognition systems.
problem Tackling the vulnerability of iris recognition systems to adversarial attacks.
method Developed a deep auto-encoder surrogate network to generate adversarial examples for iris recognition systems.
result Demonstrated that adversarial examples can fool iris recognition systems in both white-box and black-box settings.
Advbox generates adversarial examples to test neural network robustness.
problem Vulnerability of neural networks to adversarial examples.
method Tool to create adversarial examples in multiple frameworks.
result Benchmarking robustness of machine learning models.
Researchers show how to manipulate Partial Dependence plots to deceive explanations of predictive models.
problem The robustness and trustworthiness of Partial Dependence (PD) explanations are compromised.
method Data poisoning using genetic and gradient algorithms to manipulate PD plots.
result PD explanations can be fooled and manipulated to mislead understanding of predictive models.
CLEVER overestimates adversarial perturbation size due to gradient masking.
problem Overestimation of adversarial perturbation size by attack-based methods.
method CLEVER aims to provide a lower bound on perturbation size.
result CLEVER overestimates perturbation size due to gradient masking.
Adversarial perturbations fool wearable sensor systems, showing transferability across different systems.
problem Adversarial examples fool wearable sensor systems, showing transferability across different systems.
method Study of adversarial transferability in wearable sensor systems from four perspectives: systems, subjects, sensor body locations, and datasets.
result Strong untargeted transferability in most cases, targeted attacks less successful.
DCNs are fooled by Gabor noise patterns similar to adversarial perturbations.
problem Sensitivity of DCNs to Gabor noise patterns.
method Examined different DCN architectures and their sensitivity to Gabor noise.
result DCNs are fooled by Gabor noise patterns similar to adversarial perturbations.
Deep neural networks (DNNs) are powerful nonlinear architectures that are known to be robust to random perturbations of the input. However, these models are vulnerable to adversarial perturbations--small input changes crafted explicitly to fool the model. In this paper, we ask whether a DNN can distinguish adversarial …
This paper explores vulnerabilities in hierarchical graph pooling neural networks for graph classification.
problem Vulnerability of hierarchical graph pooling neural networks in graph classification tasks.
method Proposes an adversarial attack framework using a surrogate model to generate adversarial samples.
result Adversarial samples can fool hierarchical GNN-based graph classification models, demonstrating their vulnerability.
Neural networks can predict BMI from faces, and can be fooled into wrong predictions.
problem Vulnerability of BMI prediction models to adversarial attacks.
method Test-time adversarial attacks on neural networks trained to infer BMI from facial images.
result Neural networks can be tricked into predicting incorrect BMI values, posing a risk of insurance fraud.
New metric detects adversarial samples with high accuracy.
problem Vulnerability of deep neural networks to adversarial samples.
method Analyzed adversarial samples through their contributions to principal components of images.
result Proposed new metric (k,p) point for measuring robustness to adversarial samples.
Curriculum learning strategies improve GAN training speed and quality.
problem Training GANs is notoriously difficult and time-consuming.
method Proposes three curriculum learning strategies based on difficulty scores.
result Curriculum learning strategies lead to faster convergence and superior results.
Researchers create a flickering attack to fool video recognition networks.
problem Adversarial manipulation of video classification networks.
method Introducing a flickering temporal perturbation to fool video classifiers.
result Achieved high fooling ratio and temporal-invariant perturbation.
Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.
problem Symbolic knowledge in neural models can make models more susceptible to adversarial attacks.
method Investigated deep probabilistic graphical models that incorporate symbolic knowledge and neural nets.
result Symbolic knowledge can propagate the negative effects of adversarial examples, making models more vulnerable.
Paper shows LIME and SHAP explanations can be fooled by adversarial attacks.
problem Reliability of post hoc explanation methods like LIME and SHAP.
method Adversarial attack technique to hide classifier biases.
result Popular explanation techniques can be fooled into generating biased explanations.
Proposes a new method to measure classifier robustness.
problem Measuring robustness of classifiers is crucial but challenging.
method Weighting sample importance based on difficulty and using logistic regression as a theoretical case study.
result The proposed score is independent of sample choice and measures robustness effectively.
Paper explores combining auto-encoder representations to fool adversarial discriminators.
problem Combining auto-encoder representations to fool adversarial discriminators.
method Mixing function to produce interpolations of hidden states or masked combinations of latent representations.
result Shows that mixing function can produce interpolations consistent with a conditioned class label.
Universal audio perturbations fool ASR systems.
problem Speech recognition systems are vulnerable to adversarial attacks.
method Developed an algorithm to create universal perturbations that fool ASR models.
result Universal perturbations can fool ASR systems across different models.
Adversarial examples fool both computer vision and humans.
problem Vulnerability of machine learning models to adversarial examples.
method Transfer adversarial examples from known models to unknown models and match human visual processing.
result Adversarial examples influence human classifications.
Black-box adversarial examples improve ASR system accuracy.
problem Improving ASR system accuracy through targeted adversarial examples.
method Combining genetic algorithms and gradient estimation for black-box attacks.
result Achieved 89.25% targeted attack similarity with 94.6% audio file similarity.
This paper extends adversarial attacks to produce desired class probability distributions.
problem Easily fooling deep learning models with imperceptible perturbations.
method Probabilistic framework to generate desired class probability distributions.
result The ability to closely approximate any probability distribution for classes while maintaining high fooling rates and preventing detection.
One pixel attack can fool deep neural networks, showing their vulnerability.
problem Vulnerability of deep neural networks to small perturbations in input.
method Proposes a novel method using differential evolution (DE) for generating one-pixel adversarial perturbations.
result 67.97% of natural images and 16.04% of ImageNet images can be fooled by modifying just one pixel.
3D adversarial logos can fool object detectors in real-world settings.
problem Creating robust adversarial attacks in 3D rendering views.
method Constructing 3D adversarial logos via texture mapping and differentiable rendering.
result 3D adversarial logos are more versatile and robust than traditional adversarial patches.
MDGAN generates additional samples for better anomaly detection.
problem Challenges in obtaining anomalous samples for training.
method Multi-Discriminator GAN architecture with two discriminators.
result MDGAN improves anomaly detection performance.
This paper explores how facial recognition systems can be fooled by adversarial attacks.
problem Adversarial attacks on facial recognition systems.
method Applying Fast Gradient Sign Method and crafting various black-box attack algorithms.
result High levels of perturbation can significantly decrease classifier confidence and misclassification rates.
New neural networks without weight transport are more robust to adversarial attacks.
problem Existing neural networks are easily fooled by adversarial attacks.
method Trained neural networks using feedback alignment instead of weight transport.
result Neural networks without weight transport are significantly harder to fool (98% adversarial accuracy vs 0.03%).
Camera stickers can fool deep learning systems by manipulating the lens, achieving 49.6% misclassification rate.
problem The vulnerability of deep learning systems to physical adversarial attacks.
method Iterative procedure to update attack perturbation and threat model for physical realizability.
result Achieved 49.6% misclassification rate for targeted attacks on ImageNet classifiers.
Enhances adversarial examples for better transferability across models.
problem Adversarial examples often have limited black-box transferability.
method Fine-tunes existing adversarial examples by increasing perturbation on a pre-specified layer of the source model.
result Our method achieves greater black-box transferability of adversarial examples.
While deep learning is remarkably successful on perceptual tasks, it was also shown to be vulnerable to adversarial perturbations of the input. These perturbations denote noise added to the input that was generated specifically to fool the system while being quasi-imperceptible for humans. More severely, there even exi…
We propose a new algorithm for training generative adversarial networks that jointly learns latent codes for both identities (e.g. individual humans) and observations (e.g. specific photographs). By fixing the identity portion of the latent codes, we can generate diverse images of the same subject, and by fixing the ob…
The cost-benefit analysis formulates the holy trinity of objectives of project management - cost, schedule, and benefits. As our previous research has shown, ICT projects deviate from their initial cost estimate by more than 10% in 8 out of 10 cases. Academic research has argued that Optimism Bias and Black Swan Blindn…
Adversarial training helps classifiers resist universal perturbations.
problem Vulnerability of classifiers to universal perturbations.
method Adversarial training with shared adversarial examples.
result Adversarial training reduces sensitivity to universal perturbations.
New method creates universal perturbations to fool neural network interpretations.
problem Vulnerability of gradient-based saliency maps to adversarial perturbations.
method Gradient-based optimization and PCA-based approach to create UPI.
result Existence and successful application of Universal Perturbation for Interpretation (UPI).