Journal: IEEE Transactions on Information Forensics and Security

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IEEE

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ISSN

1556-6013
1556-6021

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2013 - 201942020 - 20257
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Publications1 - 10 of 11
  • Do Not Skip Over the Offline: Verifiable Silent Preprocessing From Small Security Hardware
    Item type: Journal Article
    Dong, Wentao; Xu, Lei; Zheng, Leqian; et al. (2025)
    IEEE Transactions on Information Forensics and Security
    Multi-party computation (MPC) has gained increasing attention in both research and industry, with many protocols adopting the preprocessing model to optimize online performance through the strategic use of offline-generated, data-independent correlated randomness (or correlation). However, while extensive research has been dedicated to enhancing the online phase, the equally critical offline phase remains largely overlooked. This gap imposes significant yet unaddressed challenges in both security and efficiency, hindering the practical adoption of MPC systems. To address these challenges, we build upon the pseudorandom correlation generator (PCG) concept by Boyle et al. (CRYPTO'19, FOCS'20) and propose HPCG, a programmable, verifiable, and concretely efficient PCG construction using small security hardware. Our core technique, termed verifiable silent preprocessing, enables virtually unbounded, on-demand generation of diverse correlated randomness with provable correctness while effectively reducing offline overhead in a correlation-agnostic manner. To demonstrate the benefits of our approach, we experimentally evaluate HPCG and compare it with other preprocessing techniques. We also show how HPCG can further optimize specialized secure computation tasks (e.g., shuffling and equality test) by promoting new, customized correlations, which may be of new interest.
  • Towards Shared Ownership in the Cloud
    Item type: Journal Article
    Ritzdorf, Hubert; Soriente, Claudio; Karame, Ghassan O.; et al. (2018)
    IEEE Transactions on Information Forensics and Security
  • Physically Related Functions: Exploiting Related Inputs of PUFs for Authenticated-Key Exchange
    Item type: Journal Article
    Mukhopadhyay, Debdeep; Chatterjee, Durba; Boyapally, Harishma; et al. (2022)
    IEEE Transactions on Information Forensics and Security
    This paper initiates the study of "Cryptophasia in Hardware" - a phenomenon that allows hardware circuits/devices with no pre-established secret keys to securely exchange secret information over insecure communication networks. The study of cryptophasia is motivated by the need to establish secure communication channels between lightweight resource-constrained devices incapable of securely storing cryptographic keys and/or executing resource-intensive cryptographic protocols. In this paper, we introduce a novel concept called Physically Related Functions (PReFs) that can exchange secret information in a secure and authenticated manner over insecure networks. This function can be visualized as an abstraction of Strong Physically Unclonable Functions (PUFs). Strong PUFs have the limitation in communicating between two identical devices, an issue that we address in the definition of PReFs. We describe a formal framework for analyzing the functional and security requirements of PReFs. In this framework, we present a lightweight (in terms of computation cost) yet provably secure authenticated key-exchange protocol that relies only on PReFs and makes no additional assumptions (such as secure storage of cryptographic keys). Finally, we present a proof-of-concept realization of PReFs in hardware over Digilent Cora Z7 - a low-cost development platform (consisting of an ARM Cortex processor and a Xilinx FPGA) that is particularly suitable for real-world IoT applications involving resource-constrained devices. We validate that our realization of PReFs satisfies all the properties warranted by our formal framework. We further demonstrate the efficacy of our proposed protocol by analyzing its performance (in terms of computational and communication latency) over the Digilent Cora Z7 platform.
  • Unsupervised Domain Adaptation for Face Anti-Spoofing
    Item type: Journal Article
    Li, Haoliang; Li, Wen; Cao, Hong; et al. (2018)
    IEEE Transactions on Information Forensics and Security
  • Fingerprint Liveness Detection From Single Image Using Low-Level Features and Shape Analysis
    Item type: Journal Article
    Dubey, Rohit K.; Goh, Jonathan; Thing, Vrizlynn. L.L. (2016)
    IEEE Transactions on Information Forensics and Security
  • An Obfuscator for Securing Ring Confidential Transactions' Signing Keys of Cryptocurrencies
    Item type: Journal Article
    Shi, Yang; Teng, Minyu; Luo, Tianyuan; et al. (2025)
    IEEE Transactions on Information Forensics and Security
    Ring Confidential Transaction (RingCT) protocols are widely used in cryptocurrencies to protect user privacy. Consequently, a corresponding digital signature scheme, such as a ring signature scheme that hides the signers' identities, is required. Accordingly, the security of a RingCT protocol depends on the confidentiality of the secret signing keys of the underlying ring signature scheme. However, existing solutions like hardware wallets, Trusted Execution Environments (TEEs), and threshold signature schemes have limitations such as specified expensive hardware, targeting attacks at CPUs on insufficiently secure hardware, and overheads caused by multiple parties. On the contrary, program obfuscation for signature schemes offers advantages over these existing approaches. Concretely, we propose a novel obfuscator that secures the secret keys of the concise linkable spontaneous anonymous group (CLSAG) signature scheme, which is the latest ring signature scheme used in Monero's RingCT protocol. To achieve enhanced security, the proposed obfuscator leverages Paillier homomorphic encryption to transform secret keys into an obfuscated form resistant to attacks. The security of the proposed obfuscator has been formally proved. Computational efficiency has been both theoretically analyzed and experimentally evaluated with positive results on various testing platforms.
  • Enabling Cross-Chain Transactions: A Decentralized Cryptocurrency Exchange Protocol
    Item type: Journal Article
    Tian, Hangyu; Xue, Kaiping; Luo, Xinyi; et al. (2021)
    IEEE Transactions on Information Forensics and Security
    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 third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve secure trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on an Ethereum blockchain and deployed on an Ethereum test network. In addition to implementing transactions between individual users, our scheme also allows transactions among multiple users. The experimental result proves that the cost of our scheme is acceptable.
  • SAGNet: Decoupling Semantic-Agnostic Artifacts From Limited Training Data for Robust Generalization in Deepfake Detection
    Item type: Journal Article
    Tao, Renshuai; Tan, Chuangchuang; Liu, Huan; et al. (2025)
    IEEE Transactions on Information Forensics and Security
    Deepfake detection presents a significant challenge, particularly when the available training data is constrained to a limited set of semantic categories-a common and realistic scenario. In deepfake detection, the training labels typically indicate whether an image is real or fake, without specifying the semantic content, such as object classes. Moreover, we cannot know in advance the object categories present in an image to be detected. Ideally, a deepfake detection model should perform consistently across different semantic categories during inference, irrespective of the content. However, existing methods often exhibit significant performance bias between seen and unseen classes, struggling to generalize effectively. To address this issue, we propose Semantic-AGnostic artifact Network (SAGNet), an innovative and efficient approach designed to decouple semantic-agnostic artifacts from content-specific distributions in the training data. Our method eliminates semantic-specific biases, ensuring that the model focuses on universal artifacts related to image authenticity rather than content-dependent features. By employing this decoupling strategy, SAGNet greatly enhances the model's generalization capacity, even when trained on limited data. Remarkably, through experiments, we demonstrate that SAGNet achieves performance comparable to models trained with 10 times more data, despite being trained on only 2 classes (comparing SAGNet trained on 2 classes with Ojha et al. (2023) trained on 20 categories). Furthermore, through extensive experiments, we show that SAGNet's improvements are not only evident across different semantic categories but also extend to various generative methods, including multiple GAN-based and diffusion-based models. This cross-method generalization emphasizes SAGNet's versatility and effectiveness in diverse generative scenarios. Overall, our method represents a significant advancement in deepfake detection, particularly in realistic situations where the training data is limited. The code is released at https://github.com/rstao-bjtu/SAGNet/
  • On the Security of End-to-End Measurements Based on Packet-Pair Dispersions
    Item type: Journal Article
    Karame, Ghassan O.; Danev, Boris; Bannwart, Cyrill; et al. (2013)
    IEEE Transactions on Information Forensics and Security
  • FastTextDodger: Decision-Based Adversarial Attack Against Black-Box NLP Models With Extremely High Efficiency
    Item type: Journal Article
    Hu, Xiaoxue; Liu, Geling; Zheng, Baolin; et al. (2024)
    IEEE Transactions on Information Forensics and Security
    Recently, achieving query-efficient adversarial example attacks targeting black-box natural language models has attracted widespread attention from researchers. This task is considered difficult due to the discrete nature of texts, limited knowledge of the target model, and strict query access limitations in real-world systems. However, existing attacks often require a large number of queries or result in low attack success rates, having not met practical requirements. To address this, we propose FastTextDodger, a simple and compact decision-based black-box textual adversarial attack that generates grammatically correct adversarial texts with high attack success rates and few queries. Experimental results show that FastTextDodger achieves an impressive 97.4% attack success rate on benchmark datasets and models, and only needs about 200 queries. Compared to state-of-the-art attacks, FastTextDodger only requires one-tenth of the number of queries in text classification and entailment tasks while maintaining comparable attack success rates and perturbed word rates.
Publications1 - 10 of 11