Search Results for author:
Found 15 papers, 4 papers with code
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.
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.
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.
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].
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.
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.
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.
