Biblio

The Internet of Things (IoT) is a technological vision in which constrained or embedded devices connect together through the Internet. This enables common objects to be empowered with communication and cooperation capabilities. Industry can take an enormous advantage of IoT, leading to the so-called Industrial IoT. In these systems, integrity, confidentiality, and access control over data are key requirements. An emerging approach to reach confidentiality and access control is Attribute-Based Encryption (ABE), which is a technique able to enforce cryptographically an access control over data. In this paper, we propose fABElous, an ABE scheme suitable for Industrial IoT applications which aims at minimizing the overhead of encryption on communication. fABElous ensures data integrity, confidentiality, and access control, while reducing the communication overhead of 35% with respect to using ABE techniques naively.

The wide deployment of general purpose and embedded microprocessors has emphasized the need for defenses against cyber-attacks. Due to the globalized supply chain, however, there are several stages where a processor can be maliciously modified. The most promising stage, and the hardest during which to inject the hardware trojan, is the fabrication stage. As modern microprocessor chips are characterized by very dense, billion-transistor designs, such attacks must be very carefully crafted. In this paper, we demonstrate zero overhead malicious modifications on both high-performance and embedded microprocessors. These hardware trojans enable privilege escalation through execution of an instruction stream that excites the necessary conditions to make the modification appear. The minimal footprint, however, comes at the cost of a small window of attack opportunities. Experimental results show that malicious users can gain escalated privileges within a few million clock cycles. In addition, no system crashes were reported during normal operation, rendering the modifications transparent to the end user.
 

Automated human facial image de-identification is a much needed technology for privacy-preserving social media and intelligent surveillance applications. Other than the usual face blurring techniques, in this work, we propose to achieve facial anonymity by slightly modifying existing facial images into "averaged faces" so that the corresponding identities are difficult to uncover. This approach preserves the aesthesis of the facial images while achieving the goal of privacy protection. In particular, we explore a deep learning-based facial identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining inter-identity distinctions. By suppressing and tinkering FIP features, we achieve the goal of k-anonymity facial image de-identification while preserving desired utilities. Using a face database, we successfully demonstrate that the resulting "averaged faces" will still preserve the aesthesis of the original images while defying facial image identity recognition.

In order to provide reliable security solution to the people, the concept of smart ATM security system based on Embedded Linux platform is suggested in this paper. The study is focused on Design and Implementation of Face Detection based ATM Security System using Embedded Linux Platform. The system is implemented on the credit card size Raspberry Pi board with extended capability of open source Computer Vision (OpenCV) software which is used for Image processing operation. High level security mechanism is provided by the consecutive actions such as initially system captures the human face and check whether the human face is detected properly or not. If the face is not detected properly, it warns the user to adjust him/her properly to detect the face. Still the face is not detected properly the system will lock the door of the ATM cabin for security purpose. As soon as the door is lock, the system will automatic generates 3 digit OTP code. The OTP code will be sent to the watchman's registered mobile number through SMS using GSM module which is connected with the raspberry Pi. Watchman will enter the generated OTP through keypad which is interfaced with the Pi Board. The OTP will be verified and if it is correct then door will be unlock otherwise it will remain lock.

Face is crucial for human identity, while face identification has become crucial to information security. It is important to understand and work with the problems and challenges for all different aspects of facial feature extraction and face identification. In this tutorial, we identify and discuss four research challenges in current Face Detection/Recognition research and related research areas: (1) Unavoidable Facial Feature Alterations, (2) Voluntary Facial Feature Alterations, (3) Uncontrolled Environments, and (4) Accuracy Control on Large-scale Dataset. We also direct several different applications (spin-offs) of facial feature studies in the tutorial.

The face recognition methods based on convolutional neural network have achieved great success. The existing model usually used the residual network as the core architecture. The residual network is good at reusing features, but it is difficult to explore new features. And the densely connected network can be used to explore new features. We proposed a face recognition model named Dense Face to explore the performance of densely connected network in face recognition. The model is based on densely connected convolutional neural network and composed of Dense Block layers, transition layers and classification layer. The model was trained with the joint supervision of center loss and softmax loss through feature normalization and enabled the convolutional neural network to learn more discriminative features. The Dense Face model was trained using the public available CASIA-WebFace dataset and was tested on the LFW and the CAS-PEAL-Rl datasets. Experimental results showed that the densely connected convolutional neural network has achieved higher face verification accuracy and has better robustness than other model such as VGG Face and ResNet model.

In this paper, we present an extensive evaluation of face recognition and verification approaches performed by the European COST Action MULTI-modal Imaging of FOREnsic SciEnce Evidence (MULTI-FORESEE). The aim of the study is to evaluate various face recognition and verification methods, ranging from methods based on facial landmarks to state-of-the-art off-the-shelf pre-trained Convolutional Neural Networks (CNN), as well as CNN models directly trained for the task at hand. To fulfill this objective, we carefully designed and implemented a realistic data acquisition process, that corresponds to a typical face verification setup, and collected a challenging dataset to evaluate the real world performance of the aforementioned methods. Apart from verifying the effectiveness of deep learning approaches in a specific scenario, several important limitations are identified and discussed through the paper, providing valuable insight for future research directions in the field.

An enormous number of images are currently shared through social networking services such as Facebook. These images usually contain appearance of people and may violate the people's privacy if they are published without permission from each person. To remedy this privacy concern, visual privacy protection, such as blurring, is applied to facial regions of people without permission. However, in addition to image quality degradation, this may spoil the context of the image: If some people are filtered while the others are not, missing facial expression makes comprehension of the image difficult. This paper proposes an image melding-based method that modifies facial regions in a visually unintrusive way with preserving facial expression. Our experimental results demonstrated that the proposed method can retain facial expression while protecting privacy.

When a complex scene such as rotation within a plane is encountered, the recognition rate of facial expressions will decrease much. A facial expression recognition algorithm based on CNN and LBP feature fusion is proposed in this paper. Firstly, according to the problem of the lack of feature expression ability of CNN in the process of expression recognition, a CNN model was designed. The model is composed of structural units that have two successive convolutional layers followed by a pool layer, which can improve the expressive ability of CNN. Then, the designed CNN model was used to extract the facial expression features, and local binary pattern (LBP) features with rotation invariance were fused. To a certain extent, it makes up for the lack of CNN sensitivity to in-plane rotation changes. The experimental results show that the proposed method improves the expression recognition rate under the condition of plane rotation to a certain extent and has better robustness.

In the past few years, there has been increasing interest in the perception of human expressions and mental states by machines, and Facial Expression Recognition (FER) has attracted increasing attention. Facial Action Unit (AU) is an early proposed method to describe facial muscle movements, which can effectively reflect the changes in people's facial expressions. In this paper, we propose a high-performance facial expression recognition method based on facial action unit, which can run on low-configuration computer and realize video and real-time camera FER. Our method is mainly divided into two parts. In the first part, 68 facial landmarks and image Histograms of Oriented Gradients (HOG) are obtained, and the feature values of action units are calculated accordingly. The second part uses three classification methods to realize the mapping from AUs to FER. We have conducted many experiments on the popular human FER benchmark datasets (CK+ and Oulu CASIA) to demonstrate the effectiveness of our method.

Facial expression recognition is a challenging problem in the field of computer vision. In this paper, we propose a deep learning approach that can learn the joint low-level and high-level features of human face to resolve this problem. Our deep neural networks utilize convolution and downsampling to extract the abstract and local features of human face, and reconstruct the raw input images to learn global features as supplementary information at the same time. We also add an adjustable weight in the networks when combining the two kinds of features for the final classification. The experimental results show that the proposed method can achieve good results, which has an average recognition accuracy of 93.65% on the test datasets.

Currently, fusing visible and thermal images for facial expression recognition requires two modalities during both training and testing. Visible cameras are commonly used in real-life applications, and thermal cameras are typically only available in lab situations due to their high price. Thermal imaging for facial expression recognition is not frequently used in real-world situations. To address this, we propose a novel thermally enhanced facial expression recognition method which uses thermal images as privileged information to construct better visible feature representation and improved classifiers by incorporating adversarial learning and similarity constraints during training. Specifically, we train two deep neural networks from visible images and thermal images. We impose adversarial loss to enforce statistical similarity between the learned representations of two modalities, and a similarity constraint to regulate the mapping functions from visible and thermal representation to expressions. Thus, thermal images are leveraged to simultaneously improve visible feature representation and classification during training. To mimic real-world scenarios, only visible images are available during testing. We further extend the proposed expression recognition method for partially unpaired data to explore thermal images' supplementary role in visible facial expression recognition when visible images and thermal images are not synchronously recorded. Experimental results on the MAHNOB Laughter database demonstrate that our proposed method can effectively regularize visible representation and expression classifiers with the help of thermal images, achieving state-of-the-art recognition performance.

Every so often Humans utilize non-verbal gestures (e.g. facial expressions) to express certain information or emotions. Moreover, countless face gestures are expressed throughout the day because of the capabilities possessed by humans. However, the channels of these expression/emotions can be through activities, postures, behaviors & facial expressions. Extensive research unveiled that there exists a strong relationship between the channels and emotions which has to be further investigated. An Automatic Facial Expression Recognition (AFER) framework has been proposed in this work that can predict or anticipate seven universal expressions. In order to evaluate the proposed approach, Frontal face Image Database also named as Japanese Female Facial Expression (JAFFE) is opted as input. This database is further processed with a frequency domain technique known as Discrete Cosine transform (DCT) and then classified using Artificial Neural Networks (ANN). So as to check the robustness of this novel strategy, the random trial of K-fold cross validation, leave one out and person independent methods is repeated many times to provide an overview of recognition rates. The experimental results demonstrate a promising performance of this application.

In this paper, a merged convolution neural network (MCNN) is proposed to improve the accuracy and robustness of real-time facial expression recognition (FER). Although there are many ways to improve the performance of facial expression recognition, a revamp of the training framework and image preprocessing renders better results in applications. When the camera is capturing images at high speed, however, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of human facial expression. To solve this problem, we propose a statistical method for recognition results obtained from previous images, instead of using the current recognition output. Experimental results show that the proposed method can satisfactorily recognize seven basic facial expressions in real time.

Automatic recognition of facial expression images is a challenge for computer due to variation of expression, background, position and label noise. The paper propose a new method for static facial expression recognition. Main process is to perform experiments by FER-2013 dataset, the primary mission is using our CNN model to classify a set of static images into 7 basic emotions and then achieve effective classification automatically. The two preprocessing of the faces picture have enhanced the effect of the picture for recognition. First, FER datasets are preprocessed with standard histogram eqialization. Then we employ ImageDataGenerator to deviate and rotate the facial image to enhance model robustness. Finally, the result of softmax activation function (also known as multinomial logistic regression) is stacked by SVM. The result of softmax activation function + SVM is better than softmax activation function. The accuracy of facial expression recognition achieve 68.79% on the test set.

This paper proposes a novel deep two-view approach to learn features from both visible and thermal images and leverage the commonality among visible and thermal images for facial expression recognition from visible images. The thermal images are used as privileged information, which is required only during training to help visible images learn better features and classifier. Specifically, we first learn a deep model for visible images and thermal images respectively, and use the learned feature representations to train SVM classifiers for expression classification. We then jointly refine the deep models as well as the SVM classifiers for both thermal images and visible images by imposing the constraint that the outputs of the SVM classifiers from two views are similar. Therefore, the resulting representations and classifiers capture the inherent connections among visible facial image, infrared facial image and target expression labels, and hence improve the recognition performance for facial expression recognition from visible images during testing. Experimental results on the benchmark expression database demonstrate the effectiveness of our proposed method.

The face is the most dominant and distinct communication tool of human beings. Automatic analysis of facial behavior allows machines to understand and interpret a human's states and needs for natural interactions. This research focuses on developing advanced computer vision techniques to process and analyze facial images for the recognition of various facial behaviors. Specifically, this research consists of two parts: automatic facial landmark detection and tracking, and facial behavior analysis and recognition using the tracked facial landmark points. In the first part, we develop several facial landmark detection and tracking algorithms on facial images with varying conditions, such as varying facial expressions, head poses and facial occlusions. First, to handle facial expression and head pose variations, we introduce a hierarchical probabilistic face shape model and a discriminative deep face shape model to capture the spatial relationships among facial landmark points under different facial expressions and face poses to improve facial landmark detection. Second, to handle facial occlusion, we improve upon the effective cascade regression framework and propose the robust cascade regression framework for facial landmark detection, which iteratively predicts the landmark visibility probabilities and landmark locations. The second part of this research applies our facial landmark detection and tracking algorithms to facial behavior analysis, including facial action recognition and face pose estimation. For facial action recognition, we introduce a novel regression framework for joint facial landmark detection and facial action recognition. For head pose estimation, we are working on a robust algorithm that can perform head pose estimation under facial occlusion.

Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.

With the advent of deep learning based methods, facial recognition algorithms have become more effective and efficient. However, these algorithms have usually the disadvantage of requiring the use of dedicated hardware devices, such as graphical processing units (GPUs), which pose restrictions on their usage on embedded devices with limited computational power. In this paper, we present an approach that allows building an intrusion detection system, based on face recognition, running on embedded devices. It relies on deep learning techniques and does not exploit the GPUs. Face recognition is performed using a knn classifier on features extracted from a 50-layers Residual Network (ResNet-50) trained on the VGGFace2 dataset. In our experiment, we determined the optimal confidence threshold that allows distinguishing legitimate users from intruders. In order to validate the proposed system, we created a ground truth composed of 15,393 images of faces and 44 identities, captured by two smart cameras placed in two different offices, in a test period of six months. We show that the obtained results are good both from the efficiency and effectiveness perspective.

Machine-Learning-as-a-Service has become increasingly popular, with Recommendation-as-a-Service as one of the representative examples. In such services, providing privacy protection for the users is an important topic. Reviewing privacy-preserving solutions which were proposed in the past decade, privacy and machine learning are often seen as two competing goals at stake. Though improving cryptographic primitives (e.g., secure multi-party computation (SMC) or homomorphic encryption (HE)) or devising sophisticated secure protocols has made a remarkable achievement, but in conjunction with state-of-the-art recommender systems often yields far-from-practical solutions. We tackle this problem from the direction of machine learning. We aim to design crypto-friendly recommendation algorithms, thus to obtain efficient solutions by directly using existing cryptographic tools. In particular, we propose an HE-friendly recommender system, refer to as CryptoRec, which (1) decouples user features from latent feature space, avoiding training the recommendation model on encrypted data; (2) only relies on addition and multiplication operations, making the model straightforwardly compatible with HE schemes. The properties turn recommendation-computations into a simple matrix-multiplication operation. To further improve efficiency, we introduce a sparse-quantization-reuse method which reduces the recommendation-computation time by \$9$\backslash$times\$ (compared to using CryptoRec directly), without compromising the accuracy. We demonstrate the efficiency and accuracy of CryptoRec on three real-world datasets. CryptoRec allows a server to estimate a user's preferences on thousands of items within a few seconds on a single PC, with the user's data homomorphically encrypted, while its prediction accuracy is still competitive with state-of-the-art recommender systems computing over clear data. Our solution enables Recommendation-as-a-Service on large datasets in a nearly real-time (seconds) level.

Blockchains are emerging technologies that propose new business models and value propositions. Besides their application for cryptocurrency purposes, as distributed ledgers of transactions, they enable new ways to provision trusted information in a distributed fashion. In this paper, we present our product tagging solution designed to help Small & Medium Enterprises (SMEs) protect their brands against counterfeit products and parallel markets, as well as to enhance UX (User Experience) and promote the brand and product.Our solution combines the use of DLT to assure, in a verifiable and permanent way, the trustworthiness and confidentiality of the information associated to the goods and the innovative CP-ABE encryption technique to differentiate accessibility to the product's information.

With more than a trillion web pages, there is a plethora of content available for consumption. Search Engine queries invariably lead to overwhelming information, parts of it relevant and some others irrelevant. Often the information provided can be conflicting, ambiguous, and inconsistent contributing to the loss of credibility of the content. In the past, researchers have proposed approaches for credibility assessment and enumerated factors influencing the credibility of web pages. In this work, we detailed a WEBCred framework for automated genre-aware credibility assessment of web pages. We developed a tool based on the proposed framework to extract web page features instances and identify genre a web page belongs to while assessing it's Genre Credibility Score ( GCS). We validated our approach on `Information Security' dataset of 8,550 URLs with 171 features across 7 genres. The supervised learning algorithm, Gradient Boosted Decision Tree classified genres with 88.75% testing accuracy over 10 fold cross-validation, an improvement over the current benchmark. We also examined our approach on `Health' domain web pages and had comparable results. The calculated GCS correlated 69% with crowdsourced Web Of Trust ( WOT) score and 13% with algorithm based Alexa ranking across 5 Information security groups. This variance in correlation states that our GCS approach aligns with human way ( WOT) as compared to algorithmic way (Alexa) of web assessment in both the experiments.

When supporting commercial or defense systems, a perennial challenge is providing effective test and diagnosis strategies to minimize downtime, thereby maximizing system availability. Potentially one of the most effective ways to maximize downtime is to be able to detect and isolate as many faults in a system at one time as possible. This is referred to as the "multiple-fault diagnosis" problem. While several tools have been developed over the years to assist in performing multiple-fault diagnosis, considerable work remains to provide the best diagnosis possible. Recently, a new model for evolutionary computation has been developed called the "Factored Evolutionary Algorithm" (FEA). In this paper, we combine our prior work in deriving diagnostic Bayesian networks from static fault isolation manuals and fault trees with the FEA strategy to perform abductive inference as a way of addressing the multiple-fault diagnosis problem. We demonstrate the effectiveness of this approach on several networks derived from existing, real-world FIMs.

Enhancing the security and resilience of interdependent infrastructures is crucial. In this paper, we establish a theoretical framework based on Markov decision processes (MDPs) to design optimal resiliency mechanisms for interdependent infrastructures. We use MDPs to capture the dynamics of the failure of constituent components of an infrastructure and their cyber-physical dependencies. Factored MDPs and approximate linear programming are adopted for an exponentially growing dimension of both state and action spaces. Under our approximation scheme, the optimally distributed policy is equivalent to the centralized one. Finally, case studies in a large-scale interdependent system demonstrate the effectiveness of the control strategy to enhance the network resilience to cascading failures.

Today, beyond a legitimate usage, the numerous advantages of cloud computing are exploited by attackers, and Botnets supporting DDoS attacks are among the greatest beneficiaries of this malicious use. Such a phenomena is a major issue since it strongly increases the power of distributed massive attacks while involving the responsibility of cloud service providers that do not own appropriate solutions. In this paper, we present an original approach that enables a source-based de- tection of UDP-flood DDoS attacks based on a distributed system behavior analysis. Based on a principal component analysis, our contribution consists in: (1) defining the involvement of system metrics in a botcoud's behavior, (2) showing the invariability of the factorial space that defines a botcloud activity and (3) among several legitimate activities, using this factorial space to enable a botcloud detection.