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Register a new userProtect the Intellectual Property of Dataset against Unauthorized Use
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Added by
Mingfu Xue
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Training high performance Deep Neural Networks (DNNs) models require large-scale and high-quality datasets. The expensive cost of collecting and annotating large-scale datasets make the valuable datasets can be considered as the Intellectual Property (IP) of the dataset owner. To date, almost all the copyright protection schemes for deep learning focus on the copyright protection of models, while the copyright protection of the dataset is rarely studied. In this paper, we propose a novel method to actively protect the dataset from being used to train DNN models without authorization. Experimental results on on CIFAR-10 and TinyImageNet datasets demonstrate the effectiveness of the proposed method. Compared with the model trained on clean dataset, the proposed method can effectively make the test accuracy of the unauthorized model trained on protected dataset drop from 86.21% to 38.23% and from 74.00% to 16.20% on CIFAR-10 and TinyImageNet datasets, respectively.
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Deep learning techniques have made tremendous progress in a variety of challenging tasks, such as image recognition and machine translation, during the past decade. Training deep neural networks is computationally expensive and requires both human and intellectual resources. Therefore, it is necessary to protect the intellectual property of the model and externally verify the ownership of the model. However, previous studies either fail to defend against the evasion attack or have not explicitly dealt with fraudulent claims of ownership by adversaries. Furthermore, they can not establish a clear association between the model and the creators identity. To fill these gaps, in this paper, we propose a novel intellectual property protection (IPP) framework based on blind-watermark for watermarking deep neural networks that meet the requirements of security and feasibility. Our framework accepts ordinary samples and the exclusive logo as inputs, outputting newly generated samples as watermarks, which are almost indistinguishable from the origin, and infuses these watermarks into DNN models by assigning specific labels, leaving the backdoor as the basis for our copyright claim. We evaluated our IPP framework on two benchmark datasets and 15 popular deep learning models. The results show that our framework successfully verifies the ownership of all the models without a noticeable impact on their primary task. Most importantly, we are the first to successfully design and implement a blind-watermark based framework, which can achieve state-of-art performances on undetectability against evasion attack and unforgeability against fraudulent claims of ownership. Further, our framework shows remarkable robustness and establishes a clear association between the model and the authors identity.
This paper presents a high-level circuit obfuscation technique to prevent the theft of intellectual property (IP) of integrated circuits. In particular, our technique protects a class of circuits that relies on constant multiplications, such as filters and neural networks, where the constants themselves are the IP to be protected. By making use of decoy constants and a key-based scheme, a reverse engineer adversary at an untrusted foundry is rendered incapable of discerning true constants from decoy constants. The time-multiplexed constant multiplication (TMCM) block of such circuits, which realizes the multiplication of an input variable by a constant at a time, is considered as our case study for obfuscation. Furthermore, two TMCM design architectures are taken into account; an implementation using a multiplier and a multiplierless shift-adds implementation. Optimization methods are also applied to reduce the hardware complexity of these architectures. The well-known satisfiability (SAT) and automatic test pattern generation (ATPG) attacks are used to determine the vulnerability of the obfuscated designs. It is observed that the proposed technique incurs small overheads in area, power, and delay that are comparable to the hardware complexity of prominent logic locking methods. Yet, the advantage of our approach is in the insight that constants -- instead of arbitrary circuit nodes -- become key-protected.
Ever since Machine Learning as a Service (MLaaS) emerges as a viable business that utilizes deep learning models to generate lucrative revenue, Intellectual Property Right (IPR) has become a major concern because these deep learning models can easily be replicated, shared, and re-distributed by any unauthorized third parties. To the best of our knowledge, one of the prominent deep learning models - Generative Adversarial Networks (GANs) which has been widely used to create photorealistic image are totally unprotected despite the existence of pioneering IPR protection methodology for Convolutional Neural Networks (CNNs). This paper therefore presents a complete protection framework in both black-box and white-box settings to enforce IPR protection on GANs. Empirically, we show that the proposed method does not compromise the original GANs performance (i.e. image generation, image super-resolution, style transfer), and at the same time, it is able to withstand both removal and ambiguity attacks against embedded watermarks.
Case-based learning is a powerful pedagogical method of creating dialogue between theory and practice. CBL is particularly suited to executive learning as it instigates critical discussion and draws out relevant experiences. In this paper we used a real-world case to teach Information Security Management to students in Management Information Systems. The real-world case is described in a legal indictment, T-mobile USA Inc v Huawei Device USA Inc. and Huawei Technologies Co. LTD, alleging theft of intellectual property and breaches of contract concerning confidentiality and disclosure of sensitive information. The incident scenario is interesting as it relates to a business asset that has both digital and physical components that has been compromised through an unconventional cyber-physical attack facilitated by insiders. The scenario sparked an interesting debate among students about the scope and definition of security incidents, the role and structure of the security unit, the utility of compliance-based approaches to security, and the inadequate use of threat intelligence in modern security strategies.
Rowhammer attacks that corrupt level-1 page tables to gain kernel privilege are the most detrimental to system security and hard to mitigate. However, recently proposed software-only mitigations are not effective against such kernel privilege escalation attacks. In this paper, we propose an effective and practical software-only defense, called SoftTRR, to protect page tables from all existing rowhammer attacks on x86. The key idea of SoftTRR is to refresh the rows occupied by page tables when a suspicious rowhammer activity is detected. SoftTRR is motivated by DRAM-chip-based target row refresh (ChipTRR) but eliminates its main security limitation (i.e., ChipTRR tracks a limited number of rows and thus can be bypassed by many-sided hammer). Specifically, SoftTRR protects an unlimited number of page tables by tracking memory accesses to the rows that are in close proximity to page-table rows and refreshing the page-table rows once the tracked access count exceeds a pre-defined threshold. We implement a prototype of SoftTRR as a loadable kernel module, and evaluate its security effectiveness, performance overhead, and memory consumption. The experimental results show that SoftTRR protects page tables from real-world rowhammer attacks and incurs small performance overhead as well as memory cost.
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