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IEEE Transactions on Information Forensics and Security is a peer-reviewed journal published by IEEE, covering Cryptography and Data Security, Privacy-Preserving Technologies in Data, Advanced Malware Detection Techniques. Over its lifetime it has published 6,800 papers, which have received 255,747 citations.
| Publisher | IEEE |
|---|---|
| Reference style | Numbered (IEEE) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, "A representative article title," IEEE Transactions on Information Forensics and Security, vol. 12, no. 3, pp. 45–58, 2023.
Formats any DOI in IEEE Transactions on Information Forensics and Security style. No sign-up. |
| Abbreviation (ISO-4) | TIFS |
| Publishes research in | Cryptography and Data Security Privacy-Preserving Technologies in Data Advanced Malware Detection Techniques Digital Media Forensic Detection Network Security and Intrusion Detection |
| ISSN | 1556-6013 |
| Citation impact (2-yr) | 6.02 |
| h-index | 207 |
| i10-index | 3,558 |
| Top institutions publishing here | Chinese Academy of Sciences |
| Journal website | www.signalprocessingsociety.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Federated learning (FL), as a type of distributed machine learning, is capable of significantly preserving clients’ private data from being exposed to adversaries. Nevertheless, private information can still be divulged by analyzing uploaded parameters from clients, e.g., weights trained in deep neural networks. In this paper, to effectively prevent information leakage, we propose a novel…
We describe a novel general strategy for building steganography detectors for digital images. The process starts with assembling a rich model of the noise component as a union of many diverse submodels formed by joint distributions of neighboring samples from quantized image noise residuals obtained using linear and nonlinear high-pass filters. In contrast to previous…
We present a privacy-preserving deep learning system in which many learning participants perform neural network-based deep learning over a combined dataset of all, without revealing the participants' local data to a central server. To that end, we revisit the previous work by Shokri and Shmatikov (ACM CCS 2015) and show that, with their method, local…
In this paper, we propose a new method for the problem of digital camera identification from its images based on the sensor's pattern noise. For each camera under investigation, we first determine its reference pattern noise, which serves as a unique identification fingerprint. This is achieved by averaging the noise obtained from multiple images using…
The volume of convolutional neural network (CNN) models proposed for face recognition has been continuously growing larger to better fit the large amount of training data. When training data are obtained from the Internet, the labels are likely to be ambiguous and inaccurate. This paper presents a Light CNN framework to learn a compact embedding…
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