38 research outputs found
Towards joint decoding of binary Tardos fingerprinting codes
The class of joint decoder of probabilistic fingerprinting codes is of utmost
importance in theoretical papers to establish the concept of fingerprint
capacity. However, no implementation supporting a large user base is known to
date. This article presents an iterative decoder which is, as far as we are
aware of, the first practical attempt towards joint decoding. The
discriminative feature of the scores benefits on one hand from the
side-information of previously accused users, and on the other hand, from
recently introduced universal linear decoders for compound channels. Neither
the code construction nor the decoder make precise assumptions about the
collusion (size or strategy). The extension to incorporate soft outputs from
the watermarking layer is straightforward. An extensive experimental work
benchmarks the very good performance and offers a clear comparison with
previous state-of-the-art decoders.Comment: submitted to IEEE Trans. on Information Forensics and Security. -
typos corrected, one new plot, references added about ECC based
fingerprinting code
Simplification of General Mixed Boolean-Arithmetic Expressions: GAMBA
Malware code often resorts to various self-protection techniques to
complicate analysis. One such technique is applying Mixed-Boolean Arithmetic
(MBA) expressions as a way to create opaque predicates and diversify and
obfuscate the data flow.
In this work we aim to provide tools for the simplification of nonlinear MBA
expressions in a very practical context to compete in the arms race between the
generation of hard, diverse MBAs and their analysis. The proposed algorithm
GAMBA employs algebraic rewriting at its core and extends SiMBA. It achieves
efficient deobfuscation of MBA expressions from the most widely tested public
datasets and simplifies expressions to their ground truths in most cases,
surpassing peer tools
Topology-preserving watermarking of vector graphics
Watermarking techniques for vector graphics dislocate vertices in order to embed imperceptible, yet detectable, statistical features into the input data. The embedding process may result in a change of the topology of the input data, e.g., by introducing self-intersections, which is undesirable or even disastrous for many applications. In this paper we present a watermarking framework for two-dimensional vector graphics that employs conventional watermarking techniques but still provides the guarantee that the topology of the input data is preserved. The geometric part of this framework computes so-called maximum perturbation regions (MPR) of vertices. We propose two efficient algorithms to compute MPRs based on Voronoi diagrams and constrained triangulations. Furthermore, we present two algorithms to conditionally correct the watermarked data in order to increase the watermark embedding capacity and still guarantee topological correctness. While we focus on the watermarking of input formed by straight-line segments, one of our approaches can also be extended to circular arcs. We conclude the paper by demonstrating and analyzing the applicability of our framework in conjunction with two well-known watermarking techniques
Towards Joint Tardos Decoding: The 'Don~Quixote' Algorithm
International audience'Don Quixote' is a new accusation process for Tardos traitor tracing codes which is, as far as we know, the first practical implementation of joint decoding. The first key idea is to iteratively prune the list of potential colluders to keep the computational effort tractable while going from single, to pair,\ldots to -subset joint decoding. At the same time, we include users accused in previous iterations as side-information to build a more discriminative test. The second idea, coming from the field of mismatched decoders and compound channels, is to use a linear decoder based on the worst case perceived collusion channel. The decoder is tested under two accusation policies: to catch one colluders and to catch as many colluders as possible. The probability of false positive is controlled thanks to a rare event estimator. We describe a fast implementation supporting millions of users and compare our results with two recent fingerprinting codes