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This companion technical note provides the detailed proofs of Proposition 1 and Theorem 1 in the associated paper, A Fair Incentive Mechanism for Vehicular Crowdsensing Based on Federated Learning. Proposition 1 establishes sufficient conditions under which fake participation is never optimal, thereby reducing each vehicle's decision to truthful participation or non-participation and yielding a threshold-based best response.

This document provides detailed correctness proofs for the signature verification equations and the two algorithms proposed in PPBS. It also presents detailed derivations of the computation and communication overheads.

This supplementary material provides implementation specifics and extra quality comparisons to further illustrate FaceLiVT's effectiveness and superiority over existing approaches. With these additional evaluations, we aim to offer a thorough analysis and underscore the benefits of our approach.

Supplementary Materials

Improving Feature-level Adversarial Transferability via Diversity Attribution: Supplementary Materials for Papers Submitted to ICIP2025.

In this paper, we design a new open-set method to detect deepfakes that does not assume information about the techniques behind the deepfakes generation. Contrary to existing methods, which build upon known telltales left by the deepfake creation process, we assume no prior knowledge about the sample generation, thus presenting a method for blind deepfake detection, a necessary step toward true generalization.

Federated learning (FL) has emerged as a promising paradigm for decentralized machine learning while preserving data privacy. However, under communication constraints, the standard FL protocol faces the risk of client dropout. Although some research has focused on the risk from the perspectives of communication optimization and privacy protection, it is still challenging to deal with the client dropout issue in dynamic networks, where clients may join or drop the training process at any time.

Generative Adversarial Networks (GANs) have become widely used in model training, as they can improve performance and/or protect sensitive information by generating data. However, this also raises potential risks, as malicious GANs may compromise or sabotage models by poisoning their training data. Therefore, it is important to verify the origin of a model’s training data for accountability purposes. In this work, we take the first step in the forensic analysis of models trained on GAN-generated data. Specifically, we first detect whether a model is trained on GAN-generated or real data.

In this paper, we develop a framework to achieve a desirable trade-off between fairness, inference accuracy and privacy protection in the inference as service scenario. Instead of sending raw data to the cloud, we conduct a random mapping of the data, which will increase privacy protection and mitigate bias but reduce inference accuracy. To properly address the trade-off, we formulate an optimization problem to find the optimal transformation map. As the problem is nonconvex in general, we develop an iterative algorithm to find the desired map.

Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for large-scale data, the raw data cannot be transferred to a center for analysis. Therefore, new machine learning approaches are proposed for learning from the distributed data in such settings. In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.r.t.