Lime: Data Lineage in the Malicious Environment

Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol

DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks

This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework

Deep Learning-Based Dynamic Watermarking for Secure Signal Authentication in the Internet of Things

Securing the Internet of Things (IoT) is a necessary milestone toward expediting the deployment of its applications and services. In particular, the functionality of the IoT devices is extremely dependent on the reliability of their message transmission. Cyber attacks such as data injection, eavesdropping, and man-in-the-middle threats can lead to security challenges. Securing IoT devices against such attacks requires accounting for their stringent computational power and need for low-latency operations. In this paper, a novel deep learning method is proposed for dynamic watermarking of IoT signals to detect cyber attacks. The proposed learning framework, based on a long short-term memory (LSTM) structure, enables the IoT devices to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal. This method enables the IoT's cloud center, which collects signals from the IoT devices, to effectively authenticate the reliability of the signals. Furthermore, the proposed method prevents complicated attack scenarios such as eavesdropping in which the cyber attacker collects the data from the IoT devices and aims to break the watermarking algorithm. Simulation results show that, with an attack detection delay of under 1 second the messages can be transmitted from IoT devices with an almost 100% reliability.Comment: 6 pages, 9 figure

Secure Watermarking for Multimedia Content Protection: A Review of its Benefits and Open Issues

Distribution channels such as digital music downloads, video-on-demand, multimedia social networks, pose new challenges to the design of content protection measures aimed at preventing copyright violations. Digital watermarking has been proposed as a possible brick of such protection systems, providing a means to embed a unique code, as a fingerprint, into each copy of the distributed content. However, application of watermarking for multimedia content protection in realistic scenarios poses several security issues. Secure signal processing, by which name we indicate a set of techniques able to process sensitive signals that have been obfuscated either by encryption or by other privacy-preserving primitives, may offer valuable solutions to the aforementioned issues. More specifically, the adoption of efficient methods for watermark embedding or detection on data that have been secured in some way, which we name in short secure watermarking, provides an elegant way to solve the security concerns of fingerprinting applications. The aim of this contribution is to illustrate recent results regarding secure watermarking to the signal processing community, highlighting both benefits and still open issues. Some of the most interesting challenges in this area, as well as new research directions, will also be discussed

New security and control protocol for VoIP based on steganography and digital watermarking

In this paper new security and control protocol for Voice over Internet Protocol (VoIP) service is presented. It is the alternative for the IETF's (Internet Engineering Task Force) RTCP (Real-Time Control Protocol) for real-time application's traffic. Additionally this solution offers authentication and integrity, it is capable of exchanging and verifying QoS and security parameters. It is based on digital watermarking and steganography that is why it does not consume additional bandwidth and the data transmitted is inseparably bound to the voice content.Comment: 8 pages, 4 figures, 1 tabl

TTP-free Asymmetric Fingerprinting based on Client Side Embedding

In this paper, we propose a solution for implementing an asymmetric fingerprinting protocol within a client-side embedding distribution framework. The scheme is based on two novel client-side embedding techniques that are able to reliably transmit a binary fingerprint. The first one relies on standard spread-spectrum like client-side embedding, while the second one is based on an innovative client-side informed embedding technique. The proposed techniques enable secure distribution of personalized decryption keys containing the Buyer's fingerprint by means of existing asymmetric protocols, without using a trusted third party. Simulation results show that the fingerprint can be reliably recovered by using either non-blind decoding with standard embedding or blind decoding with informed embedding, and in both cases it is robust with respect to common attacks. To the best of our knowledge, the proposed scheme is the first solution addressing asymmetric fingerprinting within a clientside framework, representing a valid solution to both customer's rights and scalability issues in multimedia content distributio

Watermark Decoding Technique using Machine Learning for Intellectual Property Protection

The Watermarking is an Intellectual Property (IP) Protection method. It can ensure Field-Programmable Gate Array (FPGA) IPs from encroachment. The IP security of equipment and programming structures is the most significant prerequisite for some FPGA licensed innovation merchants. Advanced watermarking has become a creative innovation for IP assurance as of late. This paper proposes the Publicly Verifiable Watermarking plan for licensed innovation insurance in FPGA structure. The Zero-Knowledge Verification Protocol and Data Matrix strategy are utilized in this watermarking location method. The time stepping is likewise utilized with the zero-information check convention and it can versatility oppose the delicate data spillage and implanting assaults, and is along these lines hearty to the cheating from the prover, verifier, or outsider. The encryption keys are additionally utilized with the information lattice technique and it can restrict the watermark, and make the watermark vigorous against assaults. In this proposed zero-information technique zero rate asset, timing and watermarking overhead can be accomplished. The proposed zero-information watermarking plan causes zero overhead. In this proposed information lattice technique signal-rich-workmanship code picture, can be portrayed. The proposed information network watermarking plan encodes the copyright confirmation data. The zero-information confirmation convention and information grid technique proposed in this paper is executed by MATLAB R2014a in which C programming language is utilized in it and ModelSim 10.5b in which VHDL coding is utilized in it, are running on a PC. The combination instrument Xilinx ISE 14.5 is likewise used to confirm and actualize the watermarking plan

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others