9 accepted papers
"Recently, large pre-trained foundation models have become widely adopted by machine learning practitioners for a multitude of tasks. Given that such models are publicly available, relying on their use as backbone models for downstream tasks might result in high vulnerability to adversarial attacks…
In this paper, we consider privacy-preserving compressed image sharing, where the goal is to release compressed data whilst satisfying some privacy/secrecy constraints yet ensuring image reconstruction with a defined fidelity. The privacy-preserving compressed image sharing is addressed using a mach…
In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding. The core of the framework relies on sparse coding with ambiguation (SCA) mechanism that introduces the notion of inherent shared secrecy based on the support intersectio…
This paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating a possibility of counterfeited printable graphical code detection from a machine learning perspectives. We investigate a fake generation via two different deep regeneration models and study the authen…
We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need is more severe when trying to furthermore protect the privacy-sensitive content. We therefore encode images, such that,…
This paper proposes a group membership verification protocol preventing the curious but honest server from reconstructing the enrolled signatures and inferring the identity of querying clients. The protocol quantizes the signatures into discrete embeddings, making reconstruction difficult. It also a…
The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the…
In this paper, we propose a privacy preserving framework for outsourced media search applications. Considering three parties, a data owner, clients and a server, the data owner out-sources the description of his data to an external server, which provides a search service to clients on the behalf of…
In this paper, we compare two methods that can be used by the anti-counterfeiting industry to protect physical objects, which are either based on an object's natural randomness or on artificial randomness embedded on the object. We show that the considered verification architectures rely either on a…