784 research outputs found
Watermarking FPGA Bitfile for Intellectual Property Protection
Intellectual property protection (IPP) of hardware designs is the most important requirement for many Field Programmable Gate Array (FPGA) intellectual property (IP) vendors. Digital watermarking has become an innovative technology for IPP in recent years. Existing watermarking techniques have successfully embedded watermark into IP cores. However, many of these techniques share two specific weaknesses: 1) They have extra overhead, and are likely to degrade performance of design; 2) vulnerability to removing attacks. We propose a novel watermarking technique to watermark FPGA bitfile for addressing these weaknesses. Experimental results and analysis show that the proposed technique incurs zero overhead and it is robust against removing attacks
SecMon: End-to-End Quality and Security Monitoring System
The Voice over Internet Protocol (VoIP) is becoming a more available and
popular way of communicating for Internet users. This also applies to
Peer-to-Peer (P2P) systems and merging these two have already proven to be
successful (e.g. Skype). Even the existing standards of VoIP provide an
assurance of security and Quality of Service (QoS), however, these features are
usually optional and supported by limited number of implementations. As a
result, the lack of mandatory and widely applicable QoS and security guaranties
makes the contemporary VoIP systems vulnerable to attacks and network
disturbances. In this paper we are facing these issues and propose the SecMon
system, which simultaneously provides a lightweight security mechanism and
improves quality parameters of the call. SecMon is intended specially for VoIP
service over P2P networks and its main advantage is that it provides
authentication, data integrity services, adaptive QoS and (D)DoS attack
detection. Moreover, the SecMon approach represents a low-bandwidth consumption
solution that is transparent to the users and possesses a self-organizing
capability. The above-mentioned features are accomplished mainly by utilizing
two information hiding techniques: digital audio watermarking and network
steganography. These techniques are used to create covert channels that serve
as transport channels for lightweight QoS measurement's results. Furthermore,
these metrics are aggregated in a reputation system that enables best route
path selection in the P2P network. The reputation system helps also to mitigate
(D)DoS attacks, maximize performance and increase transmission efficiency in
the network.Comment: Paper was presented at 7th international conference IBIZA 2008: On
Computer Science - Research And Applications, Poland, Kazimierz Dolny
31.01-2.02 2008; 14 pages, 5 figure
Micro protocol engineering for unstructured carriers: On the embedding of steganographic control protocols into audio transmissions
Network steganography conceals the transfer of sensitive information within
unobtrusive data in computer networks. So-called micro protocols are
communication protocols placed within the payload of a network steganographic
transfer. They enrich this transfer with features such as reliability, dynamic
overlay routing, or performance optimization --- just to mention a few. We
present different design approaches for the embedding of hidden channels with
micro protocols in digitized audio signals under consideration of different
requirements. On the basis of experimental results, our design approaches are
compared, and introduced into a protocol engineering approach for micro
protocols.Comment: 20 pages, 7 figures, 4 table
A Chaotic IP Watermarking in Physical Layout Level Based on FPGA
A new chaotic map based IP (Intellectual Property) watermarking scheme at physical design level is presented. An encrypted watermark is embedded into the physical layout of a circuit by configuring LUT (Lookup Table) as specific functions when it is placed and routed onto the FPGA (Field-Programmable Gate Array). The main contribution is the use of multiple chaotic maps in the processes of watermark design and embedding, which efficiently improves the security of watermark. A hashed chaotic sequence is used to scramble the watermark. Secondly, two pseudo-random sequences are generated by using chaotic maps. One is used to determine unused LUT locations, and the other divides the watermark into groups. The watermark identifies original owner and is difficult to detect. This scheme was tested on a Xilinx Virtex XCV600-6bg432 FPGA. The experimental results show that our method has low impact on functionality, short path delay and high robustness in comparison with other methods
Intellectual Property Protection for Deep Learning Models: Taxonomy, Methods, Attacks, and Evaluations
The training and creation of deep learning model is usually costly, thus it
can be regarded as an intellectual property (IP) of the model creator. However,
malicious users who obtain high-performance models may illegally copy,
redistribute, or abuse the models without permission. To deal with such
security threats, a few deep neural networks (DNN) IP protection methods have
been proposed in recent years. This paper attempts to provide a review of the
existing DNN IP protection works and also an outlook. First, we propose the
first taxonomy for DNN IP protection methods in terms of six attributes:
scenario, mechanism, capacity, type, function, and target models. Then, we
present a survey on existing DNN IP protection works in terms of the above six
attributes, especially focusing on the challenges these methods face, whether
these methods can provide proactive protection, and their resistances to
different levels of attacks. After that, we analyze the potential attacks on
DNN IP protection methods from the aspects of model modifications, evasion
attacks, and active attacks. Besides, a systematic evaluation method for DNN IP
protection methods with respect to basic functional metrics, attack-resistance
metrics, and customized metrics for different application scenarios is given.
Lastly, future research opportunities and challenges on DNN IP protection are
presented
Watermarking Graph Neural Networks by Random Graphs
Many learning tasks require us to deal with graph data which contains rich
relational information among elements, leading increasing graph neural network
(GNN) models to be deployed in industrial products for improving the quality of
service. However, they also raise challenges to model authentication. It is
necessary to protect the ownership of the GNN models, which motivates us to
present a watermarking method to GNN models in this paper. In the proposed
method, an Erdos-Renyi (ER) random graph with random node feature vectors and
labels is randomly generated as a trigger to train the GNN to be protected
together with the normal samples. During model training, the secret watermark
is embedded into the label predictions of the ER graph nodes. During model
verification, by activating a marked GNN with the trigger ER graph, the
watermark can be reconstructed from the output to verify the ownership. Since
the ER graph was randomly generated, by feeding it to a non-marked GNN, the
label predictions of the graph nodes are random, resulting in a low false alarm
rate (of the proposed work). Experimental results have also shown that, the
performance of a marked GNN on its original task will not be impaired.
Moreover, it is robust against model compression and fine-tuning, which has
shown the superiority and applicability.Comment: https://hzwu.github.io
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