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This paper proposes an algorithm which allows Alice to simulate the game played between her and Eve. Under the condition that the set of detectors that Alice assumes Eve to have is sufficiently rich (e.g. CNNs), and that she has an algorithm enabling to avoid detection by a single classifier (e.g adversarial embedding, gibbs sampler, dynamic STCs), the proposed algorithm converges to an efficient steganographic algorithm. This is possible by using a min max strategy which consists at each iteration in selecting the least detectable stego image for the best classifier among the set of Eve's learned classifiers. The algorithm is extensively evaluated and compared to prior arts and results show the potential to increase the practical security of classical steganographic methods. For example the error probability Perr of XU-Net on detecting stego images with payload of 0.4 bpnzAC embedded by J-Uniward and QF 75 starts at 7.1% and is increased by +13.6% to reach 20.7% after eight iterations. For the same embedding rate and for QF 95, undetectability by XU-Net with J-Uniward embedding is 23.4%, and it jumps by +25.8% to reach 49.2% at iteration 3.

The driving range of electric vehicles is an important issue. The velocity profile can have an important impact on their energy consumption and thus their driving range. In this paper, a commercial electric vehicle is studied. The model of its traction subsystems is developed using a forward approach. A specific driving cycle generator is then coupled with this model. A feedback from the model to the generator allows for recalculation of the velocity reference in case of a limitation due to the electrical drives, for a more accurate estimation of energy consumption. Different trips are then studied with this method. The maximal velocity has a strong impact on the energy consumption while the maximal acceleration has a low impact.

This work proposes a protocol to iteratively build a distortion function for adaptive steganography while increasing its practical security after each iteration. It relies on prior art on targeted attacks and iterative design of steganalysis schemes. It combines targeted attacks on a given detector with a min max strategy, which dynamically selects the most difficult stego content associated with the best classifier at each iteration. We theoretically prove the convergence, which is confirmed by the practical results. Applied on J-Uniward this new protocol increases P err from 7% to 20% estimated by Xu-Net, and from 10% to 23% for a non-targeted steganalysis by a linear classifier with GFR features. CCS CONCEPTS • Security and privacy → Domain-specific security and privacy architectures;

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The establishment of an MRI-only workflow in radiotherapy depends on the ability to generate an accurate synthetic CT (sCT) for dose calculation. Previously proposed methods have used a Generative Adversarial Network (GAN) for fast sCT generation in order to simplify the clinical workflow and reduces uncertainties. In the current paper we use a conditional Generative Adversarial Network (cGAN) framework called pix2pixHD to create a robust model prone to multicenter data. This study included T2-weighted MR and CT images of 19 patients in treatment position from 3 different sites. The cGAN was trained on 2D transverse slices of 11 patients from 2 different sites. Once trained, the network was used to generate sCT images of 8 patients coming from a third site. The Mean Absolute Errors (MAE) for each patient were evaluated between real and synthetic CTs. A radiotherapy plan was optimized on the sCT series and re-calculated on CTs to assess the dose distribution in terms of voxel-wise dose difference and Dose Volume Histograms (DVH) analysis. It takes on average of to generate a complete sCT (88 slices) for a patient on our GPU. The average MAE in HU between the sCT and actual patient CT (within the body contour) is 48.5 ± 6 HU with our method. The maximum dose difference to the target is 1.3%. This study demonstrates that an sCT can be generated in a multicentric context, with fewer pre-processing steps while being fast and accurate.

Purpose MR‐to‐CT synthesis is one of the first steps in the establishment of an MRI‐only workflow in radiotherapy. Current MR‐to‐CT synthesis methods in deep learning use unpaired MR and CT training images with a cycle generative adversarial network (CycleGAN) to minimize the effect of misalignment between paired images. However, this approach critically assumes that the underlying interdomain mapping is approximately deterministic and one‐to‐one. In the current study, we use an Augmented CycleGAN (AugCGAN) model to create a robust model that can be applied to different scanners and sequences using unpaired data. Materials and methods This study included T2‐weighted MR and CT pelvic images of 38 patients in treatment position from five different centers. The AugCGAN was trained on 2D transverse slices of 19 patients from three different sites. The network was then used to generate synthetic CT (sCT) images of 19 patients from the two other sites. Mean absolute errors (MAEs) for each patient were evaluated between real and synthetic CT images. Original treatment plans of nine patients were recalculated using sCT images to assess the dose distribution in terms of voxel‐wise dose difference, gamma, and dose–volume histogram analysis. Results The mean MAEs were 59.8 Hounsfield units (italicHU) and 65.8 HU for the first and second test sites, respectively. The maximum dose difference to the target was 1.2% with a gamma pass rate using the 3%, 3 mm criteria above 99%. The average time required to generate a complete sCT image for a patient on our GPU was 8.5 s. Conclusion This study suggests that our unpaired approach achieves good performance in generalization with respect to sCT image generation.

International audienceThe many distances defined in evidence theory provide instrumental tools to analyze and compare mass functions: they have been proposed to measure conflict, dependence or similarity in different fields (information fusion , risk analysis, machine learning). Many of their mathematical properties have been studied in the past years, yet a remaining question is to know what distance to choose in a particular problem. As a step towards answering this question, we propose to interpret distances by looking at their consistency with partial orders possessing a clear semantic. We focus on the case of in-formational partial order and on the problem of approximating initial belief functions by simpler ones. Doing so, we study which distances can be used to measure the difference of informational content between two mass functions, and which distances cannot