Study and Comparison of Surface Roughness Measurements

National audienceThis survey paper focus on recent researches whose goal is to optimize treatments on 3D meshes, thanks to a study of their surface features, and more precisely their roughness and saliency. Applications like watermarking or lossy compression can benefit from a precise roughness detection, to better hide the watermarks or quantize coarsely these areas, without altering visually the shape. Despite investigations on scale dependence leading to multi-scale approaches, an accurate roughness or pattern characterization is still lacking, but challenging for those treatments. We think there is still room for investigations that could benefit from the power of the wavelet analysis or the fractal models. Furthermore only few works are now able to differentiate roughness from saliency, though it is essential for faithfully simplifying or denoising a 3D mesh. Hence we have investigated roughness quantification methods for analog surfaces, in several domains of physics. Some roughness parameters used in these fields and the additionnal information they bring are finally studied, since we think an adaptation for 3D meshes could be beneficial

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

A robust image watermarking technique based on quantization noise visibility thresholds

International audienceA tremendous amount of digital multimedia data is broadcasted daily over the internet. Since digital data can be very quickly and easily duplicated, intellectual property right protection techniques have become important and first appeared about fifty years ago (see [I.J. Cox, M.L. Miller, The First 50 Years of Electronic Watermarking, EURASIP J. Appl. Signal Process. 2 (2002) 126-132. [52]] for an extended review). Digital watermarking was born. Since its inception, many watermarking techniques have appeared, in all possible transformed spaces. However, an important lack in watermarking literature concerns the human visual system models. Several human visual system (HVS) model based watermarking techniques were designed in the late 1990's. Due to the weak robustness results, especially concerning geometrical distortions, the interest in such studies has reduced. In this paper, we intend to take advantage of recent advances in HVS models and watermarking techniques to revisit this issue. We will demonstrate that it is possible to resist too many attacks, including geometrical distortions, in HVS based watermarking algorithms. The perceptual model used here takes into account advanced features of the HVS identified from psychophysics experiments conducted in our laboratory. This model has been successfully applied in quality assessment and image coding schemes M. Carnec, P. Le Callet, D. Barba, An image quality assessment method based on perception of structural information, IEEE Internat. Conf. Image Process. 3 (2003) 185-188, N. Bekkat, A. Saadane, D. Barba, Masking effects in the quality assessment of coded images, in: SPIE Human Vision and Electronic Imaging V, 3959 (2000) 211-219. In this paper the human visual system model is used to create a perceptual mask in order to optimize the watermark strength. The optimal watermark obtained satisfies both invisibility and robustness requirements. Contrary to most watermarking schemes using advanced perceptual masks, in order to best thwart the de-synchronization problem induced by geometrical distortions, we propose here a Fourier domain embedding and detection technique optimizing the amplitude of the watermark. Finally, the robustness of the scheme obtained is assessed against all attacks provided by the Stirmark benchmark. This work proposes a new digital rights management technique using an advanced human visual system model that is able to resist various kind of attacks including many geometrical distortions

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Efficient and Robust Video Steganography Algorithms for Secure Data Communication

Over the last two decades, the science of secretly embedding and communicating data has gained tremendous significance due to the technological advancement in communication and digital content. Steganography is the art of concealing secret data in a particular interactive media transporter such as text, audio, image, and video data in order to build a covert communication between authorized parties. Nowadays, video steganography techniques are important in many video-sharing and social networking applications such as Livestreaming, YouTube, Twitter, and Facebook because of noteworthy developments in advanced video over the Internet. The performance of any steganography method, ultimately, relies on the imperceptibility, hiding capacity, and robustness against attacks. Although many video steganography methods exist, several of them lack the preprocessing stages. In addition, less security, low embedding capacity, less imperceptibility, and less robustness against attacks are other issues that affect these algorithms. This dissertation investigates and analyzes cutting edge video steganography techniques in both compressed and raw domains. Moreover, it provides solutions for the aforementioned problems by proposing new and effective methods for digital video steganography. The key objectives of this research are to develop: 1) a highly secure video steganography algorithm based on error correcting codes (ECC); 2) an increased payload video steganography algorithm in the discrete wavelet domain based on ECC; 3) a novel video steganography algorithm based on Kanade-Lucas-Tomasi (KLT) tracking and ECC; 4) a robust video steganography algorithm in the wavelet domain based on KLT tracking and ECC; 5) a new video steganography algorithm based on the multiple object tracking (MOT) and ECC; and 6) a robust and secure video steganography algorithm in the discrete wavelet and discrete cosine transformations based on MOT and ECC. The experimental results from our research demonstrate that our proposed algorithms achieve higher embedding capacity as well as better imperceptibility of stego videos. Furthermore, the preprocessing stages increase the security and robustness of the proposed algorithms against attacks when compared to state-of-the-art steganographic methods

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea Ministry of Science, ICT & Future Planning, Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea 2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068

A Survey on Neural Trojans

Neural networks have become increasingly prevalent in many real-world applications including security-critical ones. Due to the high hardware requirement and time consumption to train high-performance neural network models, users often outsource training to a machine-learning-as-a-service (MLaaS) provider. This puts the integrity of the trained model at risk. In 2017, Liu et. al. found that, by mixing the training data with a few malicious samples of a certain trigger pattern, hidden functionality can be embedded in the trained network which can be evoked by the trigger pattern. We refer to this kind of hidden malicious functionality as neural Trojans. In this paper, we survey a myriad of neural Trojan attack and defense techniques that have been proposed over the last few years. In a neural Trojan insertion attack, the attacker can be the MLaaS provider itself or a third party capable of adding or tampering with training data. In most research on attacks, the attacker selects the Trojan\u27s functionality and a set of input patterns that will trigger the Trojan. Training data poisoning is the most common way to make the neural network acquire Trojan functionality. Trojan embedding methods that modify the training algorithm or directly interfere with the neural network\u27s execution at the binary level have also been studied. Defense techniques include detecting neural Trojans in the model and/or Trojan trigger patterns, erasing the Trojan\u27s functionality from the neural network model, and bypassing the Trojan. It was also shown that carefully crafted neural Trojans can be used to mitigate other types of attacks. We systematize the above attack and defense approaches in this paper

Automatic detection of pathological regions in medical images

Medical images are an essential tool in the daily clinical routine for the detection, diagnosis, and monitoring of diseases. Different imaging modalities such as magnetic resonance (MR) or X-ray imaging are used to visualize the manifestations of various diseases, providing physicians with valuable information. However, analyzing every single image by human experts is a tedious and laborious task. Deep learning methods have shown great potential to support this process, but many images are needed to train reliable neural networks. Besides the accuracy of the final method, the interpretability of the results is crucial for a deep learning method to be established. A fundamental problem in the medical field is the availability of sufficiently large datasets due to the variability of different imaging techniques and their configurations. The aim of this thesis is the development of deep learning methods for the automatic identification of anomalous regions in medical images. Each method is tailored to the amount and type of available data. In the first step, we present a fully supervised segmentation method based on denoising diffusion models. This requires a large dataset with pixel-wise manual annotations of the pathological regions. Due to the implicit ensemble characteristic, our method provides uncertainty maps to allow interpretability of the model’s decisions. Manual pixel-wise annotations face the problems that they are prone to human bias, hard to obtain, and often even unavailable. Weakly supervised methods avoid these issues by only relying on image-level annotations. We present two different approaches based on generative models to generate pixel-wise anomaly maps using only image-level annotations, i.e., a generative adversarial network and a denoising diffusion model. Both perform image-to-image translation between a set of healthy and a set of diseased subjects. Pixel-wise anomaly maps can be obtained by computing the difference between the original image of the diseased subject and the synthetic image of its healthy representation. In an extension of the diffusion-based anomaly detection method, we present a flexible framework to solve various image-to-image translation tasks. With this method, we managed to change the size of tumors in MR images, and we were able to add realistic pathologies to images of healthy subjects. Finally, we focus on a problem frequently occurring when working with MR images: If not enough data from one MR scanner are available, data from other scanners need to be considered. This multi-scanner setting introduces a bias between the datasets of different scanners, limiting the performance of deep learning models. We present a regularization strategy on the model’s latent space to overcome the problems raised by this multi-site setting