Image steganography aims to embed secret messages into cover images in an imperceptible manner. While steganalysis tries to identify stegos from covers, which is a special binary classification problem. Recently, some literatures show that the adversarial embedding can mislead the advanced steganalyzers based on convolutional neural network (CNN), and thus enhance the steganography security. Since

The functionality of face biometric systems is severely challenged by presentation attacks (PA’s), and especially those attacks that have not been available during the training phase of a PA detection (PAD) subsystem. Among other alternatives, the one-class classification (OCC) paradigm is an applicable strategy that has been observed to provide good generalisation against unseen attacks. Following

The new mobile edge computing (MEC) paradigm fundamentally changes the data caching technique by allowing data to be cached on edge servers attached to base stations within hundreds of meters from users. It provides a bounded latency guarantee for latency-sensitive applications, e.g., interactive AR/VR applications, online gaming, etc. However, in the highly distributed MEC environment, cache data

Recently, the application of multi-party secure computing schemes based on homomorphic encryption in the field of machine learning attracts attentions across the research fields. Previous studies have demonstrated that secure protocols adopting packed additive homomorphic encryption (PAHE) schemes based on the ring learning with errors (RLWE) problem exhibit significant practical merits, and are particularly

Age estimation is a challenging task not only because face appearance is affected by illumination, pose, and expression, but also because there exists age label ambiguity among different demographic groups. In this work, we first revisit different label distribution learning (LDL) based age estimation methods and propose a more general formulation, which can unify individual LDL-based age estimation

This paper proposes a co-design adaptive defense scheme against a class of zero-day buffer over-read attacks that follow unknown stationary probability distributions. In particular, the co-design scheme integrates an improved UCB algorithm and a customized server. The improved UCB algorithm adaptively allocates guard pages on a heap based on induced damage of the guard pages so as to minimize the accumulated

Election and voting play crucial roles in democratic society for an elactorate to make a collective decision. E-voting is one of the most challenging problems in cryptographic research to provide multiple dimensions security assurances. In this paper, we study an important voting paradigm, score voting, with privacy protection, which has not been investigated in previous work. We propose a blockchain

Matching manually cropped pedestrian images between queries and candidates, termed as person re-identification, has achieved significant progress with deep convolutional neural networks. Recently, a topic called ‘person search’ is proposed for the end-to-end application of re-identification technologies. It integrates object detection and person re-identification and aims to both locate and match pedestrians

In this paper, we study efficient differentially private alternating direction methods of multipliers (ADMM) via gradient perturbation for many centralized machine learning problems. For smooth convex loss functions with (non)-smooth regularization, we propose the first differentially private ADMM (DP-ADMM) algorithm with the performance guarantee of $(\epsilon,\delta)$ -differential privacy ( $(\epsilon

As a versatile technique, cloud-fog computing extends the traditional cloud server to offer various on-demand data services. Maintaining data confidentiality is one of the most crucial requirements for data services, many cryptosystems have been proposed to reserve information privacy against such an untrusted environment. However, in cloud-fog computing, how to confidentially and efficiently share

Easily and flexibly deployable ad-hoc communication networks emerging in tactical military or even civilian contexts, frequently suffer from poor synchronization due to lack of coordinating infrastructure. In addition to synchronization issues, and especially in military settings, security from the aspect of detectability is also of crucial importance. Imperfect synchronization can be dealt with by

Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle control, and have been used in state-of-the-art AV controllers. However, deep neural networks (DNNs) render automated driving vulnerable to machine learning-based

Along with the success of deep neural network (DNN) models, rise the threats to the integrity of these models. A recent threat is the Trojan attack where an attacker interferes with the training pipeline by inserting triggers into some of the training samples and trains the model to act maliciously only for samples that contain the trigger. Since the knowledge of triggers is privy to the attacker,

Learning local representations is important for face recognition (FR). Recent attention-based networks emphasize few facial parts, while ignoring other potentially discriminative ones. This is more serious when there are large pose variations, occlusions (e.g. face masks), or other image quality changes. To address this, we propose Diverse and Sparse Attentions, called DSA-Face. First, a divergence

Batch steganography aims at properly allocating a large payload to multiple covers, so as to keep the whole covert communication at a satisfactory level of security. JPEG is currently one of the most widely used formats for image storage and transmission. This paper presents an efficient JPEG batch steganographic scheme, which allocates the payload in a linear manner w.r.t. a new heuristic measure

As recommendation systems continue to evolve, researchers are using trust data to improve the accuracy of recommendation prediction and help users find relevant information. However, large recommendation systems with trust data suffer from the sparse trust problem, which leads to grade inflation and severely affects the reliability of trust propagation. This paper presents a novel research on sparse

Federated learning (FL), as a distributed machine learning setting, has received considerable attention in recent years. To alleviate privacy concerns, FL essentially promises that multiple parties jointly train the model by exchanging gradients rather than raw data. However, intrinsic privacy issue still exists in FL, e.g., user’s training samples could be revealed by solely inferring gradients. Moreover

One existing challenge associated with large scale skyline queries on cloud services, particularly when dealing with private information such as biomedical data, is supporting multi-party queries with curious-but-honest parties on encrypted data. In addition, existing solutions designed for performing secure skyline queries incur significant communication and computation costs due to ciphertext calculation

The problem of covert communication over Multiple-Input Multiple-Output (MIMO) Additive White Gaussian Noise (AWGN) channels is investigated, in which a transmitter attempts to reliably communicate with a legitimate receiver while avoiding detection by a passive adversary. The covert capacity of the MIMO AWGN channel is characterized under a variational distance covertness constraint when the MIMO

With the rapid development in face recognition, most of the existing systems can perform very well in unconstrained scenarios. However, it is still a very challenging task to detect face spoofing attacks, thus face anti-spoofing has become one of the most important research topics in the community. Though various anti-spoofing models have been proposed, the generalisation capability of these models

Low Entropy Masking Schemes (LEMS) had been proposed to mitigate the high-performance overhead results from the Full Entropy Masking Schemes (FEMS) while offering good protection against side-channel attacks. The masking schemes usually rely on Boolean masking, however, splitting sensitive variables in a multiplicative way is more amenable to non-linear functions and it had been applied to both software

In this paper, we present a new technique for recovering the secret inner components of block cipher structures. This technique does not simply distinguish a block cipher structure from a random permutation but recovers the secret inner components. In addition, our technique is more general than ad hoc structural cryptanalysis for specific structures. A new tool, the Inequality Constraints Table (

Leakage detection tests have become an indispensable tool for testing implementations featuring side channel countermeasures such as masking. Whilst moment-based techniques such as the Welch’s $t$ -test are universally powerful if there is leakage in a central moment, they naturally fail if this is not the case. Distribution-based techniques such as the $\chi ^{2}$ -test then come to the rescue, but

In recent years, Internet of Things (IoT) security has attracted significant interest by researchers due to new characteristics of IoT such as heterogeneity of devices, resource constraints, and new types of attacks targeting IoT. Intrusion detection, which is an indispensable part of a security system, is also included in these studies. In order to explore the complex characteristics of IoT, machine

Person Re-Identification (ReID) across non-overlapping cameras is a challenging task, and most works in prior art rely on supervised feature learning from a labeled dataset to match the same person in different views. However, it demands the time-consuming task of labeling the acquired data, prohibiting its fast deployment in forensic scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising

Radio-frequency fingerprints (RFFs) are promising solutions for realizing low-cost physical layer authentication. Machine learning-based methods have been proposed for RFF extraction and discrimination. However, most existing methods are designed for the closed-set scenario where the set of devices is remains unchanged. These methods cannot be generalized to the RFF discrimination of unknown devices

As an immutable append-only distributed ledger, blockchain allows a group of participants to reach a consensus in an untrustworthy ecosystem. Immutability is a blockchain feature that persists data forever, but it is no longer legal in reality. Blockchain has unchangeable improper contents that violate laws. Moreover, data regulation toward “the right to be forgotten” requires blockchain must be modifiable

As cryptocurrency and blockchain-related assets become more common in our digital society, there is a corresponding need to secure our digital assets, including the private keys used to secure access to such assets (e.g., due to loss or corruption of the data storage medium). However, there are limitations in existing blockchain-related asset management and recovery methods. Therefore, we use zero-knowledge

Recent research has shown that non-additive image steganographic frameworks effectively improve security performance through adjusting distortion distribution. However, as far as we know, all of the existing non-additive proposals are based on handcrafted policies, and can only be applied to a specific image domain, which heavily prevent non-additive steganography from releasing its full potentiality

Recently, collaborative distributed energy management systems (CoDEMS) have emerged as an effective solution to manage distributed energy resources in microgrid. In CoDEMS, devices collaborate in a distributive manner over communication networks to meet electrical loads and supply balance at minimum cost. However, mutual information exchanges among the devices in CoDEMS may leak important information

We present TRACE, a comprehensive provenance tracking system for scalable, real-time, enterprise-wide APT detection. TRACE uses static analysis to identify program unit structures and inter-unit dependences, such that the provenance of an output event includes the input events within the same unit. Provenance collected from individual hosts are integrated to facilitate construction of a distributed

Deep Neural Network (DNN) models have been extensively developed by companies for a wide range of applications. The development of a customized DNN model with great performance requires costly investments, and its structure (layers and hyper-parameters) is considered intellectual property and holds immense value. However, in this paper, we found the model secret is vulnerable when a cloud-based FPGA

In this study, we propose an efficient sorting method for encrypted data using fully homomorphic encryption (FHE). The proposed method extends the existing 2-way sorting method by applying the $k$ -way sorting network for any prime $k$ to reduce the depth in terms of comparison operation from $O(\log _{2}^{2}~n)$ to $O(k\log _{k}^{2}~n)$ , thereby improving performance for $k$ slightly larger than

The smart features of modern cars are enabled by a number of Electronic Control Units (ECUs) components that communicate through an in-vehicle network, known as Controller Area Network (CAN) bus. The fundamental challenge is the security of the communication link where an attacker can inject messages (e.g., increase the speed) that may impact the safety of the driver. Most of existing practical IDS

Biometric recognition technology has made significant advances over the last decade and is now used across a number of services and applications. However, this widespread deployment has also resulted in privacy concerns and evolving societal expectations about the appropriate use of the technology. For example, the ability to automatically extract age, gender, race, and health cues from biometric data

We focus on attacks against a biometric authentication system aimed at reconstructing a biometric sample of the subject from the protected template. Such systems include three blocks: feature extraction, binarization, and protection. We propose a new white-box reversing attack on the binarization block that approximates a biometric template given the binary string obtained by the binarization block

Kinship verification is a long-standing research challenge in computer vision. The visual differences presented to the face have a significant effect on the recognition capabilities of the kinship systems. We argue that aggregating multiple visual knowledge can better describe the characteristics of the subject for precise kinship identification. Typically, the age-invariant features can represent

Online Social Networks (OSNs) are witnessing sophisticated cyber threats, that are generally conducted using fake or compromised profiles. Automated agents ( aka socialbots), a category of sophisticated and modern threat entities, are the native of the social media platforms and responsible for various modern weaponized information-related attacks, such as astroturfing, misinformation diffusion, and

With the development of wireless technology, the ubiquitous sensor networks have a profound effect on the way human interacts with computers, devices and environment. In order to reduce the potentially serious risks in the interaction, applying lightweight ciphers is effective to balance security, efficiency and convenience. Simeck is such a lightweight cipher that provides data confidentiality, authentication

With the rapid development of face manipulation technology, it is difficult for human eyes to distinguish fake face images. On the contrary, Convolutional Neural Network (CNN) discriminators can quickly reach high accuracy in identifying fake/real face images. In this study, we explore the behavior of CNN models in distinguish fake/real faces. We find multi-scale texture difference information plays

We analyze a path validation scheme with constant-size proof (published in IEEE Transactions on Information Forensics and Security) and demonstrate that this scheme fails to achieve unforgeability. An adversary can forge a valid proof with a non-negligible probability.

Memory-based physical unclonable functions (mPUFs) are widely accepted as highly secure because of the unclonable and immutable nature of manufacturer process variations. Although numerous successful attacks have been proposed against PUFs, mPUFs are resistant to non-invasive attacks as the mPUF does not support the open-access protocol. Hence, existing attacks against mPUFs mostly rely on invasive/semi-invasive

Thanks to its convenience and cost-savings feature, cloud computing ushers a new era. Yet its security and privacy issues must not be neglected. Private set intersection (PSI) is useful and important in many cloud computing applications, such as document similarity, genetic paternity and data mining. The cloud server performs intersection operations on two outsourced encrypted datasets of data owners

Deep neural networks (DNNs) have been increasingly used in face recognition (FR) systems. Recent studies, however, show that DNNs are vulnerable to adversarial examples, which potentially mislead DNN-based FR systems in the physical world. Existing attacks either generate perturbations working merely in the digital world, or rely on customized equipment to generate perturbations that are not robust

Research efforts in trust evaluation has evolved to provide security in Wireless Sensor Networks (WSNs), while being dependent on external parameters and network topology. Existing node level trust evaluation in WSNs evaluate trust over 1-hop assuring trustworthiness of an immediate node. This paper proposes trust evaluation using the intrinsic property of a node memory over a multihop scenario which

This paper proposes a multi-agent deep reinforcement learning-based buffer-aided relay selection scheme for an intelligent reflecting surface (IRS)-assisted secure cooperative network in the presence of an eavesdropper. We consider a practical phase model where both phase shift and reflection amplitude are discrete variables to vary the reflection coefficients of the IRS. Furthermore, we introduce

Recently, Zhang et al. proposed a novel public data integrity verification scheme for the cloud storage using indistinguishability obfuscation ( $iO$ ), and extend it to support batch verification and data dynamic operations ( IEEE Transactions on Information Forensics and Security , vol. 12, no. 3, pp. 676–688, Mar. 2017). However, we find that the scheme has two flaws: (a) the self-checking of the

Deep neural networks (DNNs) are known to be inherently vulnerable to malicious attacks such as the adversarial attack and the backdoor attack. The former is crafted by adding small perturbations to benign inputs so as to fool a DNN. The latter generally embeds a hidden pattern in a DNN by poisoning the dataset during the training process, which causes the infected model to misbehave on predefined inputs

The electric network frequency (ENF) is an important and extensively researched forensic criterion to authenticate digital recordings, but currently it is still challenging to extract reliable ENF traces from recordings in uncontrollable environments. In this paper, we present a framework for robust ENF extraction from real-world audio recordings, featuring multi-tone harmonic ENF enhancement and graph-based

Vehicular Ad-hoc Networks (VANETs) supporting the seamless operation of autonomous vehicles introduce various network-connected devices. The widespread devices are engaged in VANETs so that users can enjoy advantageous computing and reliable services. The combination brings in massive real-time message propagation and dissemination, which would be leveraged by the adversaries to perform data association

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized

Soft-biometrics play an important role in face biometrics and related fields since these might lead to biased performances, threaten the user’s privacy, or are valuable for commercial aspects. Current face databases are specifically constructed for the development of face recognition applications. Consequently, these databases contain a large number of face images but lack in the number of attribute

By using metal blocks as the protective coating, placing the sensitive signals in last but second metal (LSM), integrating a low-cost one-time programming (OTP) cell in each PUF unit, the proposed switched-capacitor (SC) PUF can both provide sensitive anti-invasive-attack protective coating and stable key for the security chip. Moreover, the circuit parameters and the layout implementation of the SC

Used ubiquitously in a huge amount of security protocols or applications, elliptic curve cryptography (ECC) is one of the most important cryptographic primitives, featuring efficiency and short key size compared with other public-key cryptosystems such as DSA and RSA. However, as a computation-intensive public-key cryptographic primitive, ECC arithmetic is still the bottleneck that restrains the overall

In this paper, we investigate the impact of correlation between the energy and information links in wireless power transfer systems, from a physical layer security perspective. With that aim, we first determine how correlation can affect system capacity in practical energy harvesting set-ups. We quantify that even though link correlation improves the average signal-to-noise ratio (SNR) for a fixed

In recent years, the threat of profiling attacks using deep learning has emerged. Successful attacks have been demonstrated against various types of cryptographic modules. However, the application of deep learning to side-channel attacks (SCAs) is often not adequately assessed because the labels that are widely used in SCAs, such as the Hamming weight (HW) and Hamming distance (HD), follow an imbalanced

Person re-identification (re-ID) remains challenging in a real-world scenario, as it requires a trained network to generalise to totally unseen target data in the presence of variations across domains. Recently, generative adversarial models have been widely adopted to enhance the diversity of training data. These approaches, however, often fail to generalise to other domains, as existing generative

Physical unclonable functions (PUFs) produce responses by exploiting randomness that intrinsically occurs in integrated circuits due to uncontrollable variations in the manufacturing process of physical items. It is common practice that PUFs generate binary responses. Recently, it has been proposed to extract symbols from a higher-order alphabet in order to increase the length of the final response

Obfuscation or Logic locking (LL) is a technique for protecting hardware intellectual property (IP) blocks against diverse threats, including IP theft, reverse engineering, and malicious modifications. State-of-the-art locking techniques primarily focus on securing a design from unauthorized usage by disabling correct functionality – they often do not directly address hiding design intent through structural