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Found 16 papers, 5 papers with code
EvoSeed: Unveiling the Threat on Deep Neural Networks with Real-World Illusions
Deep neural networks are exploited using natural adversarial samples, which have no impact on human perception but are misclassified.
Evolutionary Algorithms
Misclassification Rate - Natural Adversarial Samples
k* Distribution: Evaluating the Latent Space of Deep Neural Networks using Local Neighborhood Analysis
While these methods effectively capture the overall sample distribution in the entire learned latent space, they tend to distort the structure of sample distributions within specific classes in the subset of the latent space.
Synthetic Shifts to Initial Seed Vector Exposes the Brittle Nature of Latent-Based Diffusion Models
To understand the impact of the initial seed vector on generated samples, we propose a reliability evaluation framework that evaluates the generated samples of a diffusion model when the initial seed vector is subjected to various synthetic shifts.
The Challenges of Image Generation Models in Generating Multi-Component Images
Recent advances in text-to-image generators have led to substantial capabilities in image generation.
Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts
Through this contribution, the paper aims to foster a deeper understanding of neural network limitations and proposes a practical approach to enhance their resilience in the face of evolving and unpredictable conditions.
Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning
Neural networks have revolutionized various domains, exhibiting remarkable accuracy in tasks like natural language processing and computer vision.
Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation
In conclusion, this work contributes to the ongoing research on Vision Transformers by introducing Dynamic Scanning Augmentation as a technique for improving the accuracy and robustness of ViT.
A reading survey on adversarial machine learning: Adversarial attacks and their understanding
A particular branch of research, Adversarial Machine Learning, exploits and understands some of the vulnerabilities that cause the neural networks to misclassify for near original input.
Deep neural network loses attention to adversarial images
We also analyse how different adversarial samples distort the attention of the neural network compared to original samples.
Representation Quality Explain Adversarial Attacks
The main idea lies in the fact that some features are present on unknown classes and that unknown classes can be defined as a combination of previous learned features without representation bias (a bias towards representation that maps only current set of input-outputs and their boundary).
Evolving Robust Neural Architectures to Defend from Adversarial Attacks
By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of concatenation layers in the search, we were able to evolve an architecture that is inherently accurate on adversarial samples.
Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences
A crucial step to understanding the rationale for this lack of robustness is to assess the potential of the neural networks' representation to encode the existing features.
Adversarial Robustness Assessment: Why both $L_0$ and $L_\infty$ Attacks Are Necessary
There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which makes assessing the robustness of algorithms a daunting task.
Self Training Autonomous Driving Agent
Intrinsically, driving is a Markov Decision Process which suits well the reinforcement learning paradigm.
HAUAR: Home Automation Using Action Recognition
Today, many of the home automation systems deployed are mostly controlled by humans.
