Augmented watermarking

This thesis provides an augmented watermarking technique wherein noise is based on the watermark added to the watermarked image so that only the end user who has the key for embedding the watermark can both remove the noise and watermark to get a final clear image. The recovery for different values of noise is observed. This system may be implemented as a basic digital rights management system by defining a regime of partial rights using overlaid watermarks, together with respectively added layers of noise, in which the rights of the users define the precision with which the signals may be viewed

MEMS sensors as physical unclonable functions

A fundamental requirement of any crypto system is that secret-key material remains securely stored so that it is robust in withstanding attacks including physical tampering. In this context, physical unclonable functions (PUFs) have been proposed to store cryptographic secrets in a particularly secure manner. In this thesis, the feasibility of using microelectromechanical systems (MEMS) sensors for secure key storage purposes is evaluated for the first time. To this end, we investigated an off-the-shelf 3-axis MEMS gyroscope design and used its properties to derive a unique fingerprint from each sensor. We thoroughly examined the robustness of the derived fingerprints against temperature variation and aging. We extracted stable keys with nearly full entropy from the fingerprints. The security level of the extracted keys lies in a range between 27 bits and 150 bits depending on the applied test conditions and the used entropy estimation method. Moreover, we provide experimental evidence that the extractable key length is higher in practice when multiple wafers are considered. In addition, it is shown that further improvements could be achieved by using more precise measurement techniques and by optimizing the MEMS design. The robustness of a MEMS PUF against tampering and malicious read-outs was tested by three different types of physical attacks. We could show that MEMS PUFs provide a high level of protection due to the sensitivity of their characteristics to disassembly.Eine grundlegende Anforderung jedes Kryptosystems ist, dass der verwendete geheime Schlüssel sicher und geschützt aufbewahrt wird. Vor diesem Hintergrund wurden physikalisch unklonbare Funktionen (PUFs) vorgeschlagen, um kryptographische Geheimnisse besonders sicher zu speichern. In dieser Arbeit wird erstmals die Verwendbarkeit von mikroelektromechanischen Systemen (MEMS) für die sichere Schlüsselspeicherung anhand eines 3-achsigen MEMS Drehratensensor gezeigt. Dabei werden die Eigenschaften der Sensoren zur Ableitung eines eindeutigen Fingerabdrucks verwendet. Die Temperatur- und Langzeitstabilität der abgeleiteten Fingerabdrücke wurde ausführlich untersucht. Aus den Fingerabdrücken wurden stabile Schlüssel mit einem Sicherheitsniveau zwischen 27 Bit und 150 Bit, abhängig von den Testbedingungen und der verwendeten Entropie-Schätzmethode, extrahiert. Außerdem konnte gezeigt werden, dass die Schlüssellänge ansteigt, je mehr Wafer betrachtet werden. Darüber hinaus wurde die Verwendung einer präziseren Messtechnik und eine Optimierung des MEMS-Designs als potentielle Verbesserungsmaßnahmen identifiziert. Die Robustheit einer MEMS PUF gegen Manipulationen und feindseliges Auslesen durch verschiedene Arten von physikalischen Angriffen wurde untersucht. Es konnte gezeigt werden, dass MEMS PUFs aufgrund der Empfindlichkeit ihrer Eigenschaften hinsichtlich einer Öffnung des Mold-Gehäuses eine hohe Widerstandsfähigkeit gegenüber invasiven Angriffen aufweisen

Secure key agreement using cancelable and noninvertible biometrics based on periodic transformation

Nowadays, many of the security-providing applications use biometric-based authentication, such as electronic banking, health and social services, commercial applications and law enforcement. However, since each person’s biometrics is unique and not replaceable, once it is compromised, it will be compromised forever. Therefore, it is indeed hard for the users to trust biometrics. To overcome this problem, in this thesis, we propose a novel protocol SKA-CaNPT: Secure Key Agreement Protocol using Cancelable and Noninvertible Biometrics based on Periodic Transformation. In this research, we use a periodic transformation function to make our biometrics cancelable and noninvertible. At the very end of our SKA-CaNPT protocol, the user and the server make an agreement on a symmetric shared key that is based on the feature points of the biometrics of the user. As a proof of concept, we apply our SKA-CaNPT protocol on fingerprints. In our protocol, after extracting minutiae from the fingerprints, we first employ a periodic transformation function and then we categorize our minutiae points in a predefined neighborhood by using a threshold-based quantization mechanism. Our SKA-CaNPT protocol runs in a round-manner and in each round, the server decides about the acceptance or rejection of the user according to the similarity score of the common minutiae. In addition, if the transformed data is compromised, it can be renewed just by changing one of the inputs of our transformation function. Besides, we apply different security analyses on our protocol. First of all, we use Shannon’s entropy to analyze the randomness of the agreed keys, and it shows that the generated keys have enough randomness. Secondly, to analyze the distinctiveness of the agreed keys, we use the Hamming distance metric, results of which show that the keys of different people are distinguishable from each other. Moreover, according to the low IKGR (Incorrect Key Generation Rate), high CKGR (Correct Key Generation Rate) and high attack complexity possessed by our SKA-CaNPT protocol, we can conclude that our scheme is secure against brute-force, replay and impersonation attacks

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

Fast Fragile Watermark Embedding and Iterative Mechanism With High Self-Restoration Performance

This paper presents a new algorithm to reduce significantly the computational cost of one of the best methods with self-recovery capabilities in the fragile watermarking literature. This is achieved by generating two sequences of reference bits associated to the 5 most significant bit-planes (MSBPs) of the image. The reference bits and some authentication bits are then allocated to the 3 least significant bit-planes (LSBPs) of the image. The receiver uses the authentication bits to localise altered pixel-blocks and then executes an iterative restoration mechanism to calculate the original value of the watermarked pixels. Experimental results demonstrate that the embedding method executes significantly faster compared to the state-of-the-art method while achieving a high restoration performance

Incremental iris recognition: A single-algorithm serial fusion strategy to optimize time complexity

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NoisFre: Noise-Tolerant Memory Fingerprints from Commodity Devices for Security Functions

Building hardware security primitives with on-device memory fingerprints is a compelling proposition given the ubiquity of memory in electronic devices, especially for low-end Internet of Things devices for which cryptographic modules are often unavailable. However, the use of fingerprints in security functions is challenged by the small, but unpredictable variations in fingerprint reproductions from the same device due to measurement noise. Our study formulates a novel and pragmatic approach to achieve highly reliable fingerprints from device memories. We investigate the transformation of raw fingerprints into a noise-tolerant space where the generation of fingerprints is intrinsically highly reliable. We derive formal performance bounds to support practitioners to easily adopt our methods for applications. Subsequently, we demonstrate the expressive power of our formalization by using it to investigate the practicability of extracting noise-tolerant fingerprints from commodity devices. Together with extensive simulations, we have employed 119 chips from five different manufacturers for extensive experimental validations. Our results, including an end-to-end implementation demonstration with a low-cost wearable Bluetooth inertial sensor capable of on-demand and runtime key generation, show that key generators with failure rates less than $10^-6$ can be efficiently obtained with noise-tolerant fingerprints with a single fingerprint snapshot to support ease-of-enrollment.Comment: Accepted to IEEE Transactions on Dependable and Secure Computing. Yansong Gao and Yang Su contributed equally to the study and are co-first authors in alphabetical orde

Non-invasive Techniques Towards Recovering Highly Secure Unclonable Cryptographic Keys and Detecting Counterfeit Memory Chips

Due to the ubiquitous presence of memory components in all electronic computing systems, memory-based signatures are considered low-cost alternatives to generate unique device identifiers (IDs) and cryptographic keys. On the one hand, this unique device ID can potentially be used to identify major types of device counterfeitings such as remarked, overproduced, and cloned. On the other hand, memory-based cryptographic keys are commercially used in many cryptographic applications such as securing software IP, encrypting key vault, anchoring device root of trust, and device authentication for could services. As memory components generate this signature in runtime rather than storing them in memory, an attacker cannot clone/copy the signature and reuse them in malicious activity. However, to ensure the desired level of security, signatures generated from two different memory chips should be completely random and uncorrelated from each other. Traditionally, memory-based signatures are considered unique and uncorrelated due to the random variation in the manufacturing process. Unfortunately, in previous studies, many deterministic components of the manufacturing process, such as memory architecture, layout, systematic process variation, device package, are ignored. This dissertation shows that these deterministic factors can significantly correlate two memory signatures if those two memory chips share the same manufacturing resources (i.e., manufacturing facility, specification set, design file, etc.). We demonstrate that this signature correlation can be used to detect major counterfeit types in a non-invasive and low-cost manner. Furthermore, we use this signature correlation as side-channel information to attack memory-based cryptographic keys. We validate our contribution by collecting data from several commercially available off-the-shelf (COTS) memory chips/modules and considering different usage-case scenarios