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Found 8 papers, 1 papers with code
Unbridled Icarus: A Survey of the Potential Perils of Image Inputs in Multimodal Large Language Model Security
Multimodal Large Language Models (MLLMs) demonstrate remarkable capabilities that increasingly influence various aspects of our daily lives, constantly defining the new boundary of Artificial General Intelligence (AGI).
Object-level Copy-Move Forgery Image Detection based on Inconsistency Mining
Additionally, we extract inconsistent regions between coarse similar regions obtained through self-correlation calculations and regions composed of prototypes.
A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles
Artificial Intelligence-Generated Content (AIGC) refers to the paradigm of automated content generation utilizing AI models.
Threats, Attacks, and Defenses in Machine Unlearning: A Survey
Machine Unlearning (MU) has gained considerable attention recently for its potential to achieve Safe AI by removing the influence of specific data from trained machine learning models.
3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder
To overcome this limitation and enhance the reconstruction of 3D structural features, we propose an innovative approach that integrates existing 2D features with 3D features to guide the model learning process.
Towards Efficient and Certified Recovery from Poisoning Attacks in Federated Learning
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model.
Secure Weighted Aggregation for Federated Learning
The situation is exacerbated by the cloud-based implementation of digital services when user data are captured and stored in distributed locations, hence aggregation of the user data for ML could be a serious breach of privacy regulations.
Cryptography and Security Distributed, Parallel, and Cluster Computing
MPC-enabled Privacy-Preserving Neural Network Training against Malicious Attack
The application of secure multiparty computation (MPC) in machine learning, especially privacy-preserving neural network training, has attracted tremendous attention from the research community in recent years.
