Search Results for author:
Found 12 papers, 5 papers with code
Attacking LLM Watermarks by Exploiting Their Strengths
Advances in generative models have made it possible for AI-generated text, code, and images to mirror human-generated content in many applications.
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses.
ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems
Vertical federated learning (VFL) system has recently become prominent as a concept to process data distributed across many individual sources without the need to centralize it.
Automated Side Channel Analysis of Media Software with Manifold Learning
Recent advances in representation learning and perceptual learning inspired us to consider the reconstruction of media inputs from side channel traces as a cross-modality manifold learning task that can be addressed in a unified manner with an autoencoder framework trained to learn the mapping between media inputs and side channel observations.
MDPFuzz: Testing Models Solving Markov Decision Processes
During fuzzing, MDPFuzz decides which mutated state to retain by measuring if it can reduce cumulative rewards or form a new state sequence.
Provably Valid and Diverse Mutations of Real-World Media Data for DNN Testing
In contrast, we discuss the feasibility of mutating real-world media data with provably high DIV and VAL based on manifold.
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion
We demonstrate that NLC is significantly correlated with the diversity of a test suite across a number of tasks (classification and generation) and data formats (image and text).
Secure Byzantine-Robust Federated Learning with Dimension-free Error
In the present work, we propose a federated learning protocol with bi-directional security guarantees.
FED-$\chi^2$: Secure Federated Correlation Test
In this paper, we propose the first secure federated $\chi^2$-test protocol, FED-$\chi^2$.
F^2ed-Learning: Good Fences Make Good Neighbors
In this paper, we present F^2ed-Learning, the first federated learning protocol simultaneously defending against both semi-honest server and Byzantine malicious clients.
Towards Bidirectional Protection in Federated Learning
At one end of the spectrum, some work uses secure aggregation techniques to hide the individual client's updates and only reveal the aggregated global update to a malicious server that strives to infer the clients' privacy from their updates.
Towards practical differentially private causal graph discovery
Causal graph discovery refers to the process of discovering causal relation graphs from purely observational data.
