Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, this setting is vulnerable to model poisoning attack, since the participants have permission to modify the model parameters. In this paper, we perform systematic investigation for such threats in federated learning and propose a novel optimization-based model poisoning attack. Different from existing methods, we primarily focus on the effectiveness, persistence and stealth of attacks. Numerical experiments demonstrate that the proposed method can not only achieve high attack success rate, but it is also stealthy enough to bypass two existing defense methods.

This paper addresses the consensus problem for nonlinear multi-agent systems suffering from attacks and communication delays. The network studied in this paper consists of two types of agents, namely, loyal agents and attack agents. The loyal agents update their states based on delayed state information exchanged with their neighbors. Meanwhile, the attack agents can strategically send messages with wrong values, or collude with other attack agents to disrupt the correct operation of the system. We design a novel delay robust secure consensus U+0028 DRSC U+0029 algorithm according to the neighboring nodes U+02BC delayed information. Convergence analysis of the system under the protocol designed is provided by using Lyapunov-Krasovskii stability theory and Barbalat-like argument approach. Finally, an example and simulation results are presented to demonstrate the effectiveness of the algorithm.

This article investigates the secure and privacy-preserving consensus problem of multiagent systems (MASs) with directed interaction topologies under multiple cyberattacks, which contain deception attacks and DoS attacks. First, a unified attack model is introduced to characterize such a multiple attack phenomenon. Besides, considering the existence of eavesdroppers who can intercept the data transmitted on the links, a fully distributed agent value reconstruction method based on the idea of state decomposition is designed to prevent the leakage of the agent's initial information. Then, a novel privacy-preserving adaptive resilient consensus algorithm (PPARCA) with certain graph robustness condition for MASs under the multiple cyberattacks is proposed. The algorithm adaptively takes different countermeasures in the face of different cyberattacks. PPARCA uses the reconstructed agents' states and combines with the modified secure acceptance and broadcast algorithm (SABA). Theoretical analysis shows that the proposed algorithm can effectively protect the privacy of the initial state of the agents, and reach resilient consensus in the face of cyberattacks. Finally, numerical simulations and Raspberry Pi MASs practical application experiments demonstrate the effectiveness of the proposed results.

The problem of authenticating a re-sampled image has been investigated over many years. However, few current research proposes a statistical model-based test, resulting in that statistical performance of the re-sampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose traces of re-sampling forgery, which is described with the distribution of residual noise. Afterwards, we propose a statistical model describing the residual noise from a re-sampled image. Then the detection problem is cast into the framework of hypothesis testing theory. By considering the image content with designing texture weight map, two types of statistical detectors are established. In an ideal context in which all distribution parameters are perfectly known, the Likelihood Ratio Test (LRT) is presented and its performance is theoretically established. An upper bound of the detection power can be successfully obtained from statistical performance of LRT. For a practical use, when the distribution parameters are not known, a generalized LRT with three different maps based on estimation of parameters is established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach.

In this paper, a novel distributed algorithm derived from the event-triggered strategy is proposed for achieving resilient consensus of multi-agent networks (MANs) under deception attacks. These malicious deception attacks are intended to interfere with the communication channel causing periods in time at which the sending information among nodes is modified. In particular, we develop an event-triggered update rule which can mitigate the influence of the attackers and at the same time reduce the computing and communication consumption. Each node chooses the instances to update its state information by checking whether its neighbor set meets a given cardinality-dependent function or not. With specified prerequisite on the coupling weights and the sampling period, the consensus achievement of the MANs is independent of the deception attacks, but strictly depends on the robustness of the interconnection topology. Simulation examples are finally given to illustrate the efficacy of the theoretical results.