Publications

2024

• Increasing Trust in the Open Source Supply Chain with Reproducible Builds and Functional Package Management
  
  • Malka Julien
  , 2024. Functional package managers (FPMs) and reproducible builds (R-B) are technologies and methodologies that are conceptually very different from the traditional software deployment model, and that have promising properties for software supply chain security. This thesis aims to evaluate the impact of FMPs and R-B on the security of the software supply chain and propose improvements to the FPM model to further improve trust in the open source supply chain. (10.1145/3639478.3639806)
  DOI : 10.1145/3639478.3639806
• Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transport
  
  • Torres Bernardo
  • Peeters Geoffroy
  • Richard Gaël
  , 2024. In neural audio signal processing, pitch conditioning has been used to enhance the performance of synthesizers. However, jointly training pitch estimators and synthesizers is a challenge when using standard audio-to-audio reconstruction loss, leading to reliance on external pitch trackers. To address this issue, we propose using a spectral loss function inspired by optimal transportation theory that minimizes the displacement of spectral energy. We validate this approach through an unsupervised autoencoding task that fits a harmonic template to harmonic signals. We jointly estimate the fundamental frequency and amplitudes of harmonics using a lightweight encoder and reconstruct the signals using a differentiable harmonic synthesizer. The proposed approach offers a promising direction for improving unsupervised parameter estimation in neural audio applications. (10.1109/ICASSP48485.2024.10447011)
  DOI : 10.1109/ICASSP48485.2024.10447011
• GLA-Grad: A Griffin-Lim Extended Waveform Generation Diffusion Model
  
  • Liu Haocheng
  • Baoueb Teysir
  • Fontaine Mathieu
  • Le Roux Jonathan
  • Richard Gael
  , 2024. Diffusion models are receiving a growing interest for a variety of signal generation tasks such as speech or music synthesis. WaveGrad, for example, is a successful diffusion model that conditionally uses the mel spectrogram to guide a diffusion process for the generation of high-fidelity audio. However, such models face important challenges concerning the noise diffusion process for training and inference, and they have difficulty generating high-quality speech for speakers that were not seen during training. With the aim of minimizing the conditioning error and increasing the efficiency of the noise diffusion process, we propose in this paper a new scheme called GLA-Grad, which consists in introducing a phase recovery algorithm such as the Griffin-Lim algorithm (GLA) at each step of the regular diffusion process. Furthermore, it can be directly applied to an already-trained waveform generation model, without additional training or fine-tuning. We show that our algorithm outperforms state-of-the-art diffusion models for speech generation, especially when generating speech for a previously unseen target speaker. (10.1109/ICASSP48485.2024.10446058)
  DOI : 10.1109/ICASSP48485.2024.10446058
• ON THE CHOICE OF THE OPTIMAL TEMPORAL SUPPORT FOR AUDIO CLASSIFICATION WITH PRE-TRAINED EMBEDDINGS
  
  • Quelennec Aurian
  • Olvera Michel
  • Peeters Geoffroy
  • Essid Slim
  , 2024. Current state-of-the-art audio analysis systems rely on pretrained embedding models, often used off-the-shelf as (frozen) feature extractors. Choosing the best one for a set of tasks is the subject of many recent publications. However, one aspect often overlooked in these works is the influence of the duration of audio input considered to extract an embedding, which we refer to as Temporal Support (TS). In this work, we study the influence of the TS for well-established or emerging pre-trained embeddings, chosen to represent different types of architectures and learning paradigms. We conduct this evaluation using both musical instrument and environmental sound datasets, namely OpenMIC, TAU Urban Acoustic Scenes 2020 Mobile, and ESC-50. We especially highlight that Audio Spectrogram Transformer-based systems (PaSST and BEATs) remain effective with smaller TS, which therefore allows for a drastic reduction in memory and computational cost. Moreover, we show that by choosing the optimal TS we reach competitive results across all tasks. In particular, we improve the state-of-the-art results on OpenMIC, using BEATs and PaSST without any fine-tuning.
• A LIGHTWEIGHT DUAL-STAGE FRAMEWORK FOR PERSONALIZED SPEECH ENHANCEMENT BASED ON DEEPFILTERNET2
  
  • Serre Thomas
  • Fontaine Mathieu
  • Benhaim Éric
  • Dutour Geoffroy
  • Essid Slim
  , 2024. Isolating the desired speaker’s voice amidst multiple speakers in a noisy acoustic context is a challenging task. Per- sonalized speech enhancement (PSE) endeavours to achieve this by leveraging prior knowledge of the speaker’s voice. Recent research efforts have yielded promising PSE mod- els, albeit often accompanied by computationally intensive architectures, unsuitable for resource-constrained embedded devices. In this paper, we introduce a novel method to per- sonalize a lightweight dual-stage Speech Enhancement (SE) model and implement it within DeepFilterNet2, a SE model renowned for its state-of-the-art performance. We seek an optimal integration of speaker information within the model, exploring different positions for the integration of the speaker embeddings within the dual-stage enhancement architec- ture. We also investigate a tailored training strategy when adapting DeepFilterNet2 to a PSE task. We show that our personalization method greatly improves the performances of DeepFilterNet2 while preserving minimal computational overhead. (10.1109/ICASSPW62465.2024.10627424)
  DOI : 10.1109/ICASSPW62465.2024.10627424
• Reproducibility of Build Environments through Space and Time
  
  • Malka Julien
  • Zacchiroli Stefano
  • Zimmermann Théo
  , 2024. Modern software engineering builds up on the composability of software components, that rely on more and more direct and transitive dependencies to build their functionalities. This principle of reusability however makes it harder to reproduce projects' build environments, even though reproducibility of build environments is essential for collaboration, maintenance and component lifetime. In this work, we argue that functional package managers provide the tooling to make build environments reproducible in space and time, and we produce a preliminary evaluation to justify this claim. Using historical data, we show that we are able to reproduce build environments of about 7 million Nix packages, and to rebuild 99.94% of the 14 thousand packages from a 6-year-old Nixpkgs revision. (10.1145/3639476.3639767)
  DOI : 10.1145/3639476.3639767
• Adapting Pitch-Based Self Supervised Learning Models for Tempo Estimation
  
  • Gagneré Antonin
  • Essid Slim
  • Peeters Geoffroy
  , 2024, pp.956-960. Tempo estimation is the task of estimating the periodicity of the dominant rhythm pulse of a music audio signal. It has therefore a close relationship with dominant pitch estimation. Recently, both tasks have been addressed in a ssl fashion so as to leverage unlabelled data for training. In this work, we study the applicability of two successful pitch-based ssl models, SPICE and PESTO, for the purpose of tempo estimation. Both successfully exploit Siamese networks with a pitch-shifting view generation between the two branches. To apply these models for tempo estimation, we represent the audio signal by the cqt of its onset-strength-function and adapt their view generation using time-stretching (instead of pitch shifting), which is efficiently implemented by shifting the cqt. In a large experiment, we show that simply adapting PESTO in this way yields superior results than the previous ssl approach to tempo estimation for most datasets used in the reference benchmark. Further, since PESTO is light-weight, requiring only a few training data, we study a new learning scheme where the downstream datasets are processed directly in a ssl fashion (without access to labels) showing that this is an interesting alternative further improving the performance for some datasets. (10.1109/ICASSP48485.2024.10447129)
  DOI : 10.1109/ICASSP48485.2024.10447129
• Blind estimation of audio effects using an auto-encoder approach and differentiable digital signal processing
  
  • Peladeau Côme
  • Peeters Geoffroy
  , 2024, pp.856-860. Blind Estimation of Audio Effects (BE-AFX) aims at estimating the audio effects (AFXs) applied to an original, unprocessed audio sample solely based on the processed audio sample. To train such a system traditional approaches optimize a loss between ground truth and estimated AFX parameters. This involves knowing the exact implementation of the AFXs used for the process. In this work, we propose an alternative solution that eliminates the requirement for knowing this implementation. Instead, we introduce an auto-encoder approach, which optimizes an audio quality metric. We explore, suggest, and compare various implementations of commonly used mastering AFXs, using differential signal processing or neural approximations. Our findings demonstrate that our auto-encoder approach yields superior estimates of the audio quality produced by a chain of AFXs, compared to the traditional parameter-based approach, even if the latter provides a more accurate parameter estimation. (10.1109/ICASSP48485.2024.10448301)
  DOI : 10.1109/ICASSP48485.2024.10448301
• ONLINE SPEAKER DIARIZATION OF MEETINGS GUIDED BY SPEECH SEPARATION
  
  • Gruttadauria Elio
  • Fontaine Mathieu
  • Essid Slim
  , 2024. Overlapped speech is notoriously problematic for speaker diarization systems. Consequently, the use of speech separation has recently been proposed to improve their performance. Although promising, speech separation models struggle with realistic data because they are trained on simulated mixtures with a fixed number of speakers. In this work, we introduce a new speech separation-guided diarization scheme suitable for the online speaker diarization of long meeting recordings with a variable number of speakers, as present in the AMI corpus. We envisage ConvTasNet and DPRNN as alternatives for the separation networks, with two or three output sources. To obtain the speaker diarization result, voice activity detection is applied on each estimated source. The final model is fine-tuned end-to-end, after first adapting the separation to real data using AMI. The system operates on short segments, and inference is performed by stitching the local predictions using speaker embeddings and incremental clustering. The results show that our system improves the state-of-the-art on the AMI headset mix, using no oracle information and under full evaluation (no collar and including overlapped speech). Finally, we show the strength of our system particularly on overlapped speech sections. (10.1109/ICASSP48485.2024.10447682)
  DOI : 10.1109/ICASSP48485.2024.10447682
• Neural Steerer: Novel steering vector synthesis with a causal neural field over frequency and direction
  
  • Carlo Diego Di
  • Nugraha Aditya Arie
  • Fontaine Mathieu
  • Bando Yoshiaki
  • Yoshii Kazuyoshi
  , 2024. We address the problem of accurately interpolating measured anechoic steering vectors with a deep learning framework called the neural field. This task plays a pivotal role in reducing the resourceintensive measurements required for precise sound source separation and localization, essential as the front-end of speech recognition. Classical approaches to interpolation rely on linear weighting of nearby measurements in space on a fixed, discrete set of frequencies. Drawing inspiration from the success of neural fields for novel view synthesis in computer vision, we introduce the neural steerer, a continuous complex-valued function that takes both frequency and direction as input and produces the corresponding steering vector. Importantly, it incorporates inter-channel phase difference information and a regularization term enforcing filter causality, essential for accurate steering vector modeling. Our experiments, conducted using a dataset of real measured steering vectors, demonstrate the effectiveness of our resolution-free model in interpolating such measurements. (10.1109/ICASSPW62465.2024.10626510)
  DOI : 10.1109/ICASSPW62465.2024.10626510
• SpecDiff-GAN: A Spectrally-Shaped Noise Diffusion GAN for Speech and Music Synthesis
  
  • Baoueb Teysir
  • Liu Haocheng
  • Fontaine Mathieu
  • Le Roux Jonathan
  • Richard Gael
  , 2024. Generative adversarial network (GAN) models can synthesize highquality audio signals while ensuring fast sample generation. However, they are difficult to train and are prone to several issues including mode collapse and divergence. In this paper, we introduce SpecDiff-GAN, a neural vocoder based on HiFi-GAN, which was initially devised for speech synthesis from mel spectrogram. In our model, the training stability is enhanced by means of a forward diffusion process which consists in injecting noise from a Gaussian distribution to both real and fake samples before inputting them to the discriminator. We further improve the model by exploiting a spectrally-shaped noise distribution with the aim to make the discriminator's task more challenging. We then show the merits of our proposed model for speech and music synthesis on several datasets. Our experiments confirm that our model compares favorably in audio quality and efficiency compared to several baselines. (10.1109/ICASSP48485.2024.10446830)
  DOI : 10.1109/ICASSP48485.2024.10446830
• Gegenbauer Graph Neural Networks for Time-Varying Signal Reconstruction
  
  • Castro-Correa Jhon
  • Giraldo Jhony
  • Badiey Mohsen
  • Malliaros Fragkiskos D.
  IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2024. Reconstructing time-varying graph signals (or graph time-series imputation) is a critical problem in machine learning and signal processing with broad applications, ranging from missing data imputation in sensor networks to time-series forecasting. Accurately capturing the spatio-temporal information inherent in these signals is crucial for effectively addressing these tasks. However, existing approaches relying on smoothness assumptions of temporal differences and simple convex optimization techniques that have inherent limitations. To address these challenges, we propose a novel approach that incorporates a learning module to enhance the accuracy of the downstream task. To this end, we introduce the Gegenbauer-based graph convolutional (GegenConv) operator, which is a generalization of the conventional Chebyshev graph convolution by leveraging the theory of Gegenbauer polynomials. By deviating from traditional convex problems, we expand the complexity of the model and offer a more accurate solution for recovering time-varying graph signals. Building upon GegenConv, we design the Gegenbauer-based time graph neural network (GegenGNN) architecture, which adopts an encoder–decoder structure. Likewise, our approach also uses a dedicated loss function that incorporates a mean squared error (MSE) component alongside Sobolev smoothness regularization. This combination enables GegenGNN to capture both the fidelity to ground truth and the underlying smoothness properties of the signals, enhancing the reconstruction performance. We conduct extensive experiments on real datasets to evaluate the effectiveness of our proposed approach. The experimental results demonstrate that GegenGNN outperforms state-of-the-art methods, showcasing its superior capability in recovering time-varying graph signals. (10.1109/TNNLS.2024.3381069)
  DOI : 10.1109/TNNLS.2024.3381069
• Quantum Talagrand, KKL and Friedgut’s Theorems and the Learnability of Quantum Boolean Functions
  
  • Rouzé Cambyse
  • Wirth Melchior
  • Zhang Haonan
  Communications in Mathematical Physics, Springer Verlag, 2024, 405 (4), pp.95. We extend three related results from the analysis of influences of Boolean functions to the quantum setting, namely the KKL theorem, Friedgut’s Junta theorem and Talagrand’s variance inequality for geometric influences. Our results are derived by a joint use of recently studied hypercontractivity and gradient estimates. These generic tools also allow us to derive generalizations of these results in a general von Neumann algebraic setting beyond the case of the quantum hypercube, including examples in infinite dimensions relevant to quantum information theory such as continuous variables quantum systems. Finally, we comment on the implications of our results as regards to noncommutative extensions of isoperimetric type inequalities, quantum circuit complexity lower bounds and the learnability of quantum observables. (10.1007/s00220-024-04981-0)
  DOI : 10.1007/s00220-024-04981-0
• The Impact of Word Splitting on the Semantic Content of Contextualized Word Representations
  
  • Soler Aina Garí
  • Labeau Matthieu
  • Clavel Chloé
  Transactions of the Association for Computational Linguistics, The MIT Press, 2024, 12, pp.299-320. When deriving contextualized word representations from language models, a decision needs to be made on how to obtain one for out-of-vocabulary (OOV) words that are segmented into subwords. What is the best way to represent these words with a single vector, and are these representations of worse quality than those of in-vocabulary words? We carry out an intrinsic evaluation of embeddings from different models on semantic similarity tasks involving OOV words. Our analysis reveals, among other interesting findings, that the quality of representations of words that are split is often, but not always, worse than that of the embeddings of known words. Their similarity values, however, must be interpreted with caution. (10.1162/tacl_a_00647)
  DOI : 10.1162/tacl_a_00647
• Impact of Radiofrequency Exposure from Mobile Phones on the Risk of Developing Brain Tumors in Korean and Japanese Adolescents: A MOBI-Kids Case-Control Study
  
  • Kojimahara Noriko
  • Lee Yong-Han
  • Lee Ae-Kyoung
  • Bae Sanghyuk
  • Kwon Ho-Jang
  • Ha Mina
  • Sato Yasuto
  • Taki Masao
  • Wiart Joe
  • Langer C.E.
  • Cardis Elisabeth
  Journal of Epidemiology, Japan Epidemiological Association, 2024, 34 (4), pp.180-186. Background: This study aimed to examine the association between risk of brain tumors and radiofrequency (RF) exposure from mobile phones among young people in Korea and Japan. Methods: This case-control study of brain tumors in young people was conducted in Korea and Japan under the framework of the international MOBI-Kids study. We included 118 patients diagnosed with brain tumors between 2011 and 2015 and 236 matched appendicitis controls aged 10–24 years. Information on mobile phone use was collected through face-to-face interviews. A detailed RF exposure algorithm, based on the MOBI-Kids algorithm and modified to account for the specificities of Japanese and Korean phones and networks, was used to calculate the odds ratios (ORs) for total cumulative specific energy using conditional logistic regression. Results: The adjusted ORs in the highest tertile of cumulative call time at 1 year before the reference date were 1.61 (95% confidence interval [CI], 0.72-3.60) for all brain tumors and 0.70 (95% CI, 0.16-3.03) for gliomas, with no indication of a trend with exposure. The ORs for glioma specifically, were below 1 in the lowest exposure category. Conclusions: This study provided no evidence of a causal association between mobile phone use and risk of brain tumors as a whole or glioma specifically. Further research will be required to evaluate the impact of newer technologies of communication in the future. (10.2188/jea.JE20230005)
  DOI : 10.2188/jea.JE20230005
• A multi band flexible N-path filter suited for non-contiguous channel aggregation
  
  • Jabbour Chadi
  , 2024, pp.1-6. This paper presents a novel approach to build multiband filters using N-path architecture. The proposed technique is based on using several bandpass N-path filters in parallel all centred at the same frequency but with different bandwidths. The signal reconstruction is achieved by subtracting/summing the different channels. The proposed solution offers several advantages. First as all the N-path networks are clocked using the same frequency, the constraints on the clock generation are kept low. Second, the solution offers high flexibility as it is based on N-path filters which can be easily reconfigured using their sampling frequency. Third, thanks to its multi-path architecture, it overcomes the switch ON resistance induced limitation of out-of-band attenuation thereby allowing to use smaller switches. (10.1109/ISQED60706.2024.10528677)
  DOI : 10.1109/ISQED60706.2024.10528677
• TEE-Time: A Dynamic Cache Timing Analysis Tool for Trusted Execution Environments
  
  • Forcioli Quentin
  • Chaudhuri Sumanta
  • Danger Jean-Luc
  , 2024 (25), pp.1-8. In this article, we present a tool to analyze cache timing vulnerabilities in trusted execution environments. First, we present a platform based on the well-known gem5 simulator capable of booting GlobalPlatform Compliant TEEs for ARMV8 architecture. Next, we present the associated GDB instrumentation which allows us to dynamically reconfigure the gem5 simulator and access detailed micro-architectural state after each simulation step. Unmodified Linux/TEE binaries can be run on this platform, from which detailed execution and cache access traces are gathered and analyzed on-the-fly.We demonstrate the usage of this tool, first with an in-vitro experiment to explain the concepts of Key-Cache lines, Key-Execution Points, a method to rank these lines in an increasing order of vulnerability, and code coverage. We show that real vulnerabilities can be detected with our tool, in an otherwise constant-time RSA implementation inside an open Source TEE called OP-TEE. (10.1109/ISQED60706.2024.10528744)
  DOI : 10.1109/ISQED60706.2024.10528744
• Alvolution - al and digital technologies in the European Union
  
  • Poptcheva Eva
  • Vanderborght Bram
  • Colom Miguel
  • Binkytė Rūta
  • Balalau Oana
  • Goga Oana
  • Debar Hervé
  • Coupechoux Marceau
  • Herrera Juan
  , 2024. This document compiles the extended versions of the main contributions presented in the scientific event 'AIvolution', on AI and digital technologies in the EU, organized by MEP Eva-Maria Poptcheva (Renew Europe) and Juan-Antonio Cordero-Fuertes (IP Paris), and hosted at the European Parliament (EP), on November 16th, 2023.
• A fully differentiable model for unsupervised singing voice separation
  
  • Richard Gael
  • Chouteau Pierre
  • Torres Bernardo
  , 2024. A novel model was recently proposed by Schulze-Forster et al. in [1] for unsupervised music source separation. This model allows to tackle some of the major shortcomings of existing source separation frameworks. Specifically, it eliminates the need for isolated sources during training, performs efficiently with limited data, and can handle homogeneous sources (such as singing voice). But, this model relies on an external multipitch estimator and incorporates an Ad hoc voice assignment procedure. In this paper, we propose to extend this framework and to build a fully differentiable model by integrating a multipitch estimator and a novel differentiable assignment module within the core model. We show the merits of our approach through a set of experiments, and we highlight in particular its potential for processing diverse and unseen data. (10.1109/ICASSP48485.2024.10447244)
  DOI : 10.1109/ICASSP48485.2024.10447244
• Structure-informed Positional Encoding for Music Generation
  
  • Agarwal Manvi
  • Wang Changhong
  • Richard Gaël
  , 2024. Music generated by deep learning methods often suffers from a lack of coherence and long-term organization. Yet, multi-scale hierarchical structure is a distinctive feature of music signals. To leverage this information, we propose a structure-informed positional encoding framework for music generation with Transformers. We design three variants in terms of absolute, relative and non-stationary positional information. We comprehensively test them on two symbolic music generation tasks: next-timestep prediction and accompaniment generation. As a comparison, we choose multiple baselines from the literature and demonstrate the merits of our methods using several musically-motivated evaluation metrics. In particular, our methods improve the melodic and structural consistency of the generated pieces. (10.1109/ICASSP48485.2024.10448149)
  DOI : 10.1109/ICASSP48485.2024.10448149
• MERLIN-Seg: self-supervised despeckling for label-efficient semantic segmentation
  
  • Dalsasso Emanuele
  • Rambour Clément
  • Trouvé Nicolas
  • Thome Nicolas
  Computer Vision and Image Understanding, Elsevier, 2024, 241. Remote sensing satellites acquire a continuous stream of data on a daily basis. As most of those data are unlabeled, the development of algorithms requiring weak supervision is of paramount importance. In this paper, we show that the need for annotation for Synthetic Aperture Radar data can be reduced by coupling a despeckling task (self-supervised) and a segmentation task (supervised). The proposed self-supervised learning framework, called MERLIN-Seg, has been trained for building footprint extraction and achieves favorable performances even with 1% of annotated data. We show that conditioning the network on despeckling without labels is beneficial for supervised segmentation. Our experiments demonstrate that the joint training of the two tasks achieves better performances than a vanilla segmentation network in terms of IoU, F1 score, and accuracy on both simulated and real SAR images. (10.1016/j.cviu.2024.103940)
  DOI : 10.1016/j.cviu.2024.103940
• Cybersécurité des maisons intelligentes : architectures, solutions et technologies
  
  • Khatoun Rida
  , 2024, pp.1-314. Les maisons intelligentes utilisent des dispositifs connectés à Internet, l’intelligence artificielle, des protocoles et de multiples technologies afin de permettre à leurs résidents de surveiller à distance leurs maisons et de les gérer à l’aide d’un Smartphone. Amazon, Apple et Google, entre autres, ont lancé leurs propres dispositifs pour maisons intelligentes et de nouvelles solutions sont en cours de développement. Cependant, le nombre de cyberattaques à l’encontre des maisons intelligentes est en augmentation, et ce dans le but d’accéder à des informations privées ou de commettre des actes de vandalisme numérique. Par conséquent, choisir une bonne architecture de cybersécurité pour la maison et comprendre les risques sont autant de points essentiels à étudier avant de connecter une maison à Internet. Cet ouvrage s’adresse aux personnes qui souhaitent comprendre les architectures, les protocoles et les différentes technologies utilisés dans une maison intelligente. Il présente également des solutions proposées par la communauté de recherche pour contrer ces risques.
• Effets environnementaux de la 5G (Partie 1) : La technologie 5G
  
  • Ciblat Philippe
  • Combaz Jacques
  • Coupechoux Marceau
  • Marquet Kevin
  • Orgerie Anne-Cécile
  1024 : Bulletin de la Société Informatique de France, Société Informatique de France, 2024 (23), pp.53-72. Ce document a pour objectif de rassembler les connaissances actuelles liées aux effets environnementaux de la 5G. Il s’articule autour de questions liées à ces effets. Pour cela, il est organisé en trois partie. La première présentée ici donne les bases nécessaires à la compréhension de la technologie elle-même. La deuxième détaillera les services rendus possibles par la 5G. La dernière se penchera enfin sur les effets environnementaux du déploiement de la 5G. (10.48556/SIF.1024.23.53)
  DOI : 10.48556/SIF.1024.23.53
• Absorptive nature of scattering coefficients in stress-energy tensor formalism for room acoustics
  
  • Polack Jean-Dominique
  • Dujourdy Hugo
  • Badeau Roland
  Journal of the Acoustical Society of America, Acoustical Society of America, 2024, 155 (4), pp.2339 - 2346. In the stress-energy tensor formalism, the symmetry between absorption and scattering coefficients, as proven by measurements combined with simulations, is counterintuitive. By introducing the wall admittance, we show that the scattering coefficient is partly created by the real part of the wall admittance combined with the active intensity, that is, is partly due to absorption. However, for curved surfaces or finite source distances, it also depends on the imaginary part of the wall admittance in combination with the reactive intensity, which confers its genuine scattering properties inversely proportional to the distances to the sources. Thus, for plane waves impinging on plane boundaries, or purely real admittances, scattering reduces to absorption. (10.1121/10.0025468)
  DOI : 10.1121/10.0025468
• Software countermeasures against the multiple instructions skip fault model
  
  • Khuat Vanthanh
  • Dutertre Jean-Max
  • Danger Jean-Luc
  Microelectronics Reliability, Elsevier, 2024, 155, pp.115370. In this work, we proposed two software countermeasures (CMs) for the detection of multiple instructions skips caused by Fault Injection (FI). The first CM is based on code duplication and uses a hardware dedicated counter. The implementation of this method consists in the duplication of instructions previously turned into an idempotent form and the insertion of dedicated instructions incrementing a hardware counter in between the groups of duplicated instructions. The second CM is based on the insertion of Sensitive instruction (SI)s into a block of instructions as sensors of instruction skips. The SI is chosen based on the observed Fault Model (FM) at bit level. We experimentally validated the effectiveness of the two CMs in a 32-bit Microcontroller Unit (MCU) using Laser Fault Injection (LFI) and Electromagnetic Fault Injection (EMFI). First, the skip of multiple instructions was obtained with a fault rate of 100%. The FM at bit level was identified to be bit-reset rather than bit-set. Second, we carried out LFI and EMFI experiments to the protected codes to validate the effectiveness of the CMs. In both cases, the results showed that the proposed methods are effective to detect multiple instructions skip faults. (10.1016/j.microrel.2024.115370)
  DOI : 10.1016/j.microrel.2024.115370