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Found 10 papers, 2 papers with code
Stealix: Model Stealing via Prompt Evolution
Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information.
Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment
Experiments on the IU X-RAY and MIMIC-CXR radiology datasets demonstrate that Adversarial Domain Alignment enables attackers to steal the medical MLLM without any access to medical data.
Stealthy Imitation: Reward-guided Environment-free Policy Stealing
Deep reinforcement learning policies, which are integral to modern control systems, represent valuable intellectual property.
Revisiting Neural Program Smoothing for Fuzzing
In this paper, we conduct the most extensive evaluation of NPS fuzzers against standard gray-box fuzzers (>11 CPU years and >5. 5 GPU years), and make the following contributions: (1) We find that the original performance claims for NPS fuzzers do not hold; a gap we relate to fundamental, implementation, and experimental limitations of prior works.
Adversarial Robustness Toolbox v1.0.0
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning
Deep Learning models are vulnerable to adversarial examples, i. e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence.
Open-World Visual Recognition Using Knowledge Graphs
First, a knowledge-graph representation is learned to embed a large set of entities into a semantic space.
Efficient Defenses Against Adversarial Attacks
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat.
Similarity Learning for Time Series Classification
In this paper, we propose a novel method for learning similarities based on DTW, in order to improve time series classification.
Algorithmic Robustness for Learning via $(ε, γ, τ)$-Good Similarity Functions
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking.
