Search Results for author:
Found 36 papers, 13 papers with code
TURBO: The Swiss Knife of Auto-Encoders
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods.
Stochastic Digital Twin for Copy Detection Patterns
Copy detection patterns (CDP) present an efficient technique for product protection against counterfeiting.
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR).
Ranked #2 on
Unsupervised Image Classification
on STL-10
Mathematical model of printing-imaging channel for blind detection of fake copy detection patterns
Nowadays, copy detection patterns (CDP) appear as a very promising anti-counterfeiting technology for physical object protection.
Digital twins of physical printing-imaging channel
In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a. k. a.
Printing variability of copy detection patterns
Since digital off-set printing represents great flexibility in terms of product personalized in comparison with traditional off-set printing, it looks very interesting to address the above concerns for digital off-set printers that are used by several companies for the CDP protection of physical objects.
Anomaly localization for copy detection patterns through print estimations
Systems based on classical supervised learning and digital templates assume knowledge of fake CDP at training time and cannot generalize to unseen types of fakes.
Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility
In this work, we propose a general family of optimization problems, termed as complexity-leakage-utility bottleneck (CLUB) model, which (i) provides a unified theoretical framework that generalizes most of the state-of-the-art literature for the information-theoretic privacy models, (ii) establishes a new interpretation of the popular generative and discriminative models, (iii) constructs new insights to the generative compression models, and (iv) can be used in the fair generative models.
Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach
While Deep Learning (DL) can be used as a part of the authentication system, to the best of our knowledge, none of the previous works has studied the performance of a DL-based authentication system against ML-based attacks on CDP with 1x1 symbol size.
Mobile authentication of copy detection patterns
In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications.
Turbo-Sim: a generalised generative model with a physical latent space
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model.
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
The InfoSCC-GAN architecture is based on an unsupervised contrastive encoder built on the InfoNCE paradigm, an attribute classifier and an EigenGAN generator.
Funnels: Exact maximum likelihood with dimensionality reduction
Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model.
Machine learning attack on copy detection patterns: are 1x1 patterns cloneable?
Nowadays, the modern economy critically requires reliable yet cheap protection solutions against product counterfeiting for the mass market.
Variational Leakage: The Role of Information Complexity in Privacy Leakage
We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer.
Privacy-Preserving Near Neighbor Search via Sparse Coding with Ambiguation
In this paper, we propose a framework for privacy-preserving approximate near neighbor search via stochastic sparsifying encoding.
Privacy-Preserving Image Sharing via Sparsifying Layers on Convolutional Groups
We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups.
Information bottleneck through variational glasses
In particular, we present a new interpretation of VAE family based on the IB framework using a direct decomposition of mutual information terms and show some interesting connections to existing methods such as VAE [2; 3], beta-VAE [11], AAE [12], InfoVAE [5] and VAE/GAN [13].
$ρ$-VAE: Autoregressive parametrization of the VAE encoder
We make a minimal, but very effective alteration to the VAE model.
Single-Component Privacy Guarantees in Helper Data Systems and Sparse Coding with Ambiguation
We investigate the privacy of two approaches to (biometric) template protection: Helper Data Systems and Sparse Ternary Coding with Ambiguization.
Robustification of deep net classifiers by key based diversified aggregation with pre-filtering
The robustness of the system is achieved by a specially designed key based randomization.
Reconstruction of Privacy-Sensitive Data from Protected Templates
In this paper, we address the problem of data reconstruction from privacy-protected templates, based on recent concept of sparse ternary coding with ambiguization (STCA).
Defending against adversarial attacks by randomized diversification
The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications.
Clonability of anti-counterfeiting printable graphical codes: a machine learning approach
In recent years, printable graphical codes have attracted a lot of attention enabling a link between the physical and digital worlds, which is of great interest for the IoT and brand protection applications.
Network Parameter Learning Using Nonlinear Transforms, Local Representation Goals and Local Propagation Constraints
In this paper, we introduce a novel concept for learning of the parameters in a neural network.
Clustering with Jointly Learned Nonlinear Transforms Over Discriminating Min-Max Similarity/Dissimilarity Assignment
We introduce a clustering principle that is based on evaluation of a parametric min-max measure for the discriminative prior.
Classification by Re-generation: Towards Classification Based on Variational Inference
We also introduce a criterion based on Kullback-Leibler divergence to reject doubtful examples.
Bridging machine learning and cryptography in defence against adversarial attacks
The majority of the proposed existing adversarial attacks are based on the differentiability of the DNN cost function. Defence strategies are mostly based on machine learning and signal processing principles that either try to detect-reject or filter out the adversarial perturbations and completely neglect the classical cryptographic component in the defence.
Network Learning with Local Propagation
This paper presents a locally decoupled network parameter learning with local propagation.
Learning non-linear transform with discriminative and minimum information loss priors
A novel measure related to the discriminative prior is proposed and defined on the support intersection for the transform representations.
A multi-layer network based on Sparse Ternary Codes for universal vector compression
We present the multi-layer extension of the Sparse Ternary Codes (STC) for fast similarity search where we focus on the reconstruction of the database vectors from the ternary codes.
Privacy Preserving Identification Using Sparse Approximation with Ambiguization
The sparsifying transform and privacy amplification are not symmetric for the data owner and data user.
A multi-layer image representation using Regularized Residual Quantization: application to compression and denoising
A learning-based framework for representation of domain-specific images is proposed where joint compression and denoising can be done using a VQ-based multi-layer network.
Regularized Residual Quantization: a multi-layer sparse dictionary learning approach
Furthermore, we also propose a general-purpose pre-processing for natural images which makes them suitable for such quantization.
Sparse Ternary Codes for similarity search have higher coding gain than dense binary codes
This paper addresses the problem of Approximate Nearest Neighbor (ANN) search in pattern recognition where feature vectors in a database are encoded as compact codes in order to speed-up the similarity search in large-scale databases.
