IEEE Transactions on Information Forensics and Security

Technical Co-Sponsors:

  • IEEE Communications Society
  • IEEE Computational Intelligence Society
  • IEEE Computer Society
  • IEEE Engineering in Medicine and Biology Society
  • IEEE Information Theory Society

Scope

The IEEE Transactions on Information Forensics and Security (TIFS) covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features.

For new submissions, go to Scholar One.

Reproducible research

The Transactions encourages authors to make their publications reproducible by making all information needed to reproduce the presented results available online. This typically requires publishing the code and data used to produce the publication`s figures and tables on a website; see the supplemental materials section of the information for authors. It gives other researchers easier access to the work, and facilitates fair comparisons. Recognizing the peculiarities of papers presenting deep learning techniques, the editorial board of T-IFS drafted a document with specific instructions about the information to be provided to allow research reproducibility in this kind of papers.

Multimedia content

It is now possible to submit for review and publish in Xplore supporting multimedia material such as speech samples, images, movies, matlab code etc. A multimedia graphical abstract can also be displayed along with the traditional text. More information is available under Multimedia Materials at the IEEE Author Center.

TIFS Volume 19 | 2024

WF-Transformer: Learning Temporal Features for Accurate Anonymous Traffic Identification by Using Transformer Networks

Website Fingerprinting (WF) is a network traffic mining technique for anonymous traffic identification, which enables a local adversary to identify the target website that an anonymous network user is browsing. WF attacks based on deep convolutional neural networks (CNN) get the state-of-the-art anonymous traffic classification performance. However, due to the locality restriction of CNN architecture for feature extraction on sequence data, these methods ignore the temporal feature extraction in the anonymous traffic analysis.

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Gait Attribute Recognition: A New Benchmark for Learning Richer Attributes From Human Gait Patterns

Compared to gait recognition, Gait Attribute Recognition (GAR) is a seldom-investigated problem. However, since gait attribute recognition can provide richer and finer semantic descriptions, it is an indispensable part of building intelligent gait analysis systems. Nonetheless, the types of attributes considered in the existing datasets are very limited.

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BG: A Modular Treatment of BFT Consensus Toward a Unified Theory of BFT Replication

We provide an expressive framework that allows analyzing and generating provably secure, state-of-the-art Byzantine fault-tolerant (BFT) protocols over graph of nodes, a notion formalized in the HotStuff protocol. Our framework is hierarchical, including three layers. The top layer is used to model the message pattern and abstract core functions on which BFT algorithms can be built.

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TIFS Volume 18 | 2023

Practical Public Template Attack Attacks on CRYSTALS-Dilithium With Randomness Leakages

Side-channel security has become a significant concern in the NIST post-quantum cryptography standardization process. The lattice-based CRYSTALS-Dilithium (abbr. Dilithium) becomes the primary signature standard algorithm recommended by NIST for most use cases in July 2022 due to its excellent performance in security and efficiency. Compared to Dilithium’s rich theoretical security analysis results, the side-channel security of its physical implementations needs to be further explored.

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