1,007 research outputs found
ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training
Due to costly efforts during data acquisition and model training, Deep Neural
Networks (DNNs) belong to the intellectual property of the model creator.
Hence, unauthorized use, theft, or modification may lead to legal
repercussions. Existing DNN watermarking methods for ownership proof are often
non-intuitive, embed human-invisible marks, require trust in algorithmic
assessment that lacks human-understandable attributes, and rely on rigid
thresholds, making it susceptible to failure in cases of partial watermark
erasure.
This paper introduces ClearMark, the first DNN watermarking method designed
for intuitive human assessment. ClearMark embeds visible watermarks, enabling
human decision-making without rigid value thresholds while allowing
technology-assisted evaluations. ClearMark defines a transposed model
architecture allowing to use of the model in a backward fashion to interwove
the watermark with the main task within all model parameters. Compared to
existing watermarking methods, ClearMark produces visual watermarks that are
easy for humans to understand without requiring complex verification algorithms
or strict thresholds. The watermark is embedded within all model parameters and
entangled with the main task, exhibiting superior robustness. It shows an
8,544-bit watermark capacity comparable to the strongest existing work.
Crucially, ClearMark's effectiveness is model and dataset-agnostic, and
resilient against adversarial model manipulations, as demonstrated in a
comprehensive study performed with four datasets and seven architectures.Comment: 20 pages, 18 figures, 4 table
Symmetry-Adapted Machine Learning for Information Security
Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis
FedCIP: Federated Client Intellectual Property Protection with Traitor Tracking
Federated learning is an emerging privacy-preserving distributed machine
learning that enables multiple parties to collaboratively learn a shared model
while keeping each party's data private. However, federated learning faces two
main problems: semi-honest server privacy inference attacks and malicious
client-side model theft. To address privacy inference attacks, parameter-based
encrypted federated learning secure aggregation can be used. To address model
theft, a watermark-based intellectual property protection scheme can verify
model ownership. Although watermark-based intellectual property protection
schemes can help verify model ownership, they are not sufficient to address the
issue of continuous model theft by uncaught malicious clients in federated
learning. Existing IP protection schemes that have the ability to track
traitors are also not compatible with federated learning security aggregation.
Thus, in this paper, we propose a Federated Client-side Intellectual Property
Protection (FedCIP), which is compatible with federated learning security
aggregation and has the ability to track traitors. To the best of our
knowledge, this is the first IP protection scheme in federated learning that is
compatible with secure aggregation and tracking capabilities
Cyber Security
This open access book constitutes the refereed proceedings of the 17th International Annual Conference on Cyber Security, CNCERT 2021, held in Beijing, China, in AJuly 2021. The 14 papers presented were carefully reviewed and selected from 51 submissions. The papers are organized according to the following topical sections: ​data security; privacy protection; anomaly detection; traffic analysis; social network security; vulnerability detection; text classification
IP protection for DSP algorithms\u27 FPGA implementation.
With today\u27s system-on-chip (SOC) technology, we are able to design larger and more complicated application-specific integrated circuits (ASICs) and field programmable gate array (FPGA) in shorter time period. The key point of the success of SOC technology is the reuse of intellectual property (IP) cores. Consequently the copyright protection for these IP cores becomes the major concern for the development pace of SOC technology. Watermarking technology has been proved to be an effective way of copyright protection. In this thesis, the author presents two new watermarking algorithms respectively at algorithm level and FPGA layout level. The simulations and implementation results show that the new proposals have much less design and hardware implementation overheads, lower watermark embedding and extraction cost, as well as higher security strength, compared to the previously proposed methods.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .D39. Source: Masters Abstracts International, Volume: 43-03, page: 0929. Advisers: H. K. Kwan; H. Wu. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004
- …