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Found 11 papers, 1 papers with code
Promptly Yours? A Human Subject Study on Prompt Inference in AI-Generated Art
The emerging field of AI-generated art has witnessed the rise of prompt marketplaces, where creators can purchase, sell, or share prompts for generating unique artworks.
Unintentional Security Flaws in Code: Automated Defense via Root Cause Analysis
To address these challenges, we conducted a comprehensive study evaluating the efficacy of existing methods in helping junior developers secure their code.
Spiking Neural Networks in Vertical Federated Learning: Performance Trade-offs
We implement two different federated learning architectures -- with model splitting and without model splitting -- that have different privacy and performance implications.
We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs
The reliance of popular programming languages such as Python and JavaScript on centralized package repositories and open-source software, combined with the emergence of code-generating Large Language Models (LLMs), has created a new type of threat to the software supply chain: package hallucinations.
An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
Second, the emergence of \textit{vision foundation models} -- machine learning models trained on broad data that can be easily adapted to several downstream tasks -- can be misused by attackers to craft adversarial deepfakes that can evade existing defenses.
Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks
By means of a comprehensive set of simulations of the proposed game model, we assess different factors that can impact the capability of an adversary to launch MIA in such repeated interaction settings.
BayBFed: Bayesian Backdoor Defense for Federated Learning
However, as these approaches directly operate on client updates, their effectiveness depends on factors such as clients' data distribution or the adversary's attack strategies.
A Game-theoretic Understanding of Repeated Explanations in ML Models
This paper formally models the strategic repeated interactions between a system, comprising of a machine learning (ML) model and associated explanation method, and an end-user who is seeking a prediction/label and its explanation for a query/input, by means of game theory.
Zoom on the Keystrokes: Exploiting Video Calls for Keystroke Inference Attacks
Due to recent world events, video calls have become the new norm for both personal and professional remote communication.
On the Feasibility of Sybil Attacks in Shard-Based Permissionless Blockchains
To overcome this, committee-based approaches (e. g., Elastico) that partition the outstanding transaction set into shards and (randomly) select multiple committees to process these transactions in parallel have been proposed and have become very popular.
Cryptography and Security
A Game-Theoretic Analysis of Shard-Based Permissionless Blockchains
However, one significant research gap is a lack of understanding of the strategic behavior of rational processors within committees in such shard-based consensus protocols.
Computer Science and Game Theory
