Towards Traitor Tracing in Black-and-White-Box DNN Watermarking with Tardos-based Codes

The growing popularity of Deep Neural Networks, which often require computationally expensive training and access to a vast amount of data, calls for accurate authorship verification methods to deter unlawful dissemination of the models and identify the source of the leak. In DNN watermarking the owner may have access to the full network (white-box) or only be able to extract information from its output to queries (black-box), but a watermarked model may include both approaches in order to gather sufficient evidence to then gain access to the network. Although there has been limited research in white-box watermarking that considers traitor tracing, this problem is yet to be explored in the black-box scenario. In this paper, we propose a black-and-white-box watermarking method that opens the door to collusion-resistant traitor tracing in black-box, exploiting the properties of Tardos codes, and making it possible to identify the source of the leak before access to the model is granted. While experimental results show that the method can successfully identify traitors, even when further attacks have been performed, we also discuss its limitations and open problems for traitor tracing in black-box.Comment: This work has been submitted to the IEEE International Workshop on Information Forensics and Security (WIFS) 2023 for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Pressure dependence of raman modes in double wall carbon nanotubes filled with 1D tellurium

The preparation of highly anisotropic one-dimensional (1D) structures confined into carbon nanotubes (CNTs) in general is a key objective in nanoscience. In this work, capillary effect was used to fill double wall carbon nanotubes (DWCNTs) with trigonal Tellurium. The samples are characterized by high resolution transmission electronic microscopy and Raman spectroscopy. In order to investigate their structural stability and unravel the differences induced by intershell interactions, unpolarized Raman spectra of radial and tangential modes of DWCNTs filled with 1D nanocrystalline Te excited with 514 nm were studied at room temperature and high pressure. Up to 11 GPa we found a pressure coefficient of 3.7 cm−1 GPa−1 for the internal tube and 7 cm−1 GPa−1 for the external tube. In addition, the tangential band of the external and internal tubes broaden and decrease in amplitude. All findings lead to the conclusion that the outer tube acts as a protection shield for the inner tube (at least up 11 GPa). No pressure-induced structural phase transition was observed in the studied range

Improving the reference network in wide-area Persistent Scatterer Interferometry for non-urban areas

Advanced Interferometric SAR (InSAR) technique, namely, Persistent Scatterer Interferometry (PSI), allows long term deformation time series analysis with millimeter accuracy. Reference network arcs construction, arcs estimation and integration for PSs are an important step in PSI. In rural regions, low density of PSs leads to separate clusters during reference network construction. Also, in case of wide-area PSI using ERS-1/2 or Sentinel-1 data, the computational load can be very high. Due to this, the reference network processing is usually divided into overlapping blocks and merged later. This can however lead to spatial error propagation. This paper presents algorithms for improving the reference network in wide-area PSI, with a focus on non-urban areas

Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability

As biometric technology is increasingly deployed, it will be common to replace parts of operational systems with newer designs. The cost and inconvenience of reacquiring enrolled users when a new vendor solution is incorporated makes this approach difficult and many applications will require to deal with information from different sources regularly. These interoperability problems can dramatically affect the performance of biometric systems and thus, they need to be overcome. Here, we describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion algorithms when biometric signals were generated using several biometric devices in mismatched conditions. Quality measures from the raw biometric data are available to allow system adjustment to changing quality conditions due to device changes. This system adjustment is referred to as quality-based conditional processing. The proposed fusion approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios. This allows the easy and efficient combination of matching scores from different devices assuming low dependence among modalities. In our system, quality information is used to switch between different system modules depending on the data source (the sensor in our case) and to reject channels with low quality data during the fusion. We compare our fusion approach to a set of rule-based fusion schemes over normalized scores. Results show that the proposed approach outperforms all the rule-based fusion schemes. We also show that with the quality-based channel rejection scheme, an overall improvement of 25% in the equal error rate is obtained.Comment: Published at IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Human

Synthesis of superparamagnetic iron(III) oxide nanowires in double-walled carbon nanotubes

The synthesis and characterization of superparamagnetic iron(III) oxide nanowires confined within double-walled carbon nanotubes by capillary filling with a melted precursor (iron iodide) followed by thermal treatment is reported for the first time