This work presents a power side-channel analysis (SCA) of a lightweight cryptography (LWC) algorithm, XOODYAK, implemented on an FPGA. First, we perform generic leakage detection tests for two phases of authenticated encryption with associated data (AEAD) mode, namely INITIALIZE, and ABSORB. Second, we develop novel hypothetical attack models for correlation power analysis (CPA) and demonstrate a success rate (SR) of 92%/82% and minimum-traces-to-disclosure (MTD)=13K/38K on the INITIALIZE/ABSORB phases, respectively. Third, we evaluate ABSORB against Profiled SCA using convolutional neural network (CNN), and achieve SR=96%/64% and MTD=2K/16K on the test set for the same/different keys used for training, respectively. Finally, we suggest low-overhead countermeasures to protect against these SCA attacks.

In this work, we address the challenges of side channel analysis (SCA) and fault injection (FI) in the pre-silicon design stage, particularly for cryptographic implementations. We show that the industry perspective uncovers different challenges than those usually tackled by academia, emphasizing the need for practical and scalable solutions. We present (Side Channel Attack Testbench Emulator) SCATE, a framework for detecting and mitigating SCA vulnerabilities, leveraging industry-standard electronic design automation (EDA) software for efficient power estimation and FI simulations. Moreover, we demonstrate a significant performance increase in power simulation tools by optimizing EDA algorithms for SCA applications, resulting in faster power trace generation. Additionally, we discuss the application of formal property verification (FPV) for verifying fault injection countermeasures and present a case study on an AES S-box. These approaches enable non-security-expert designers to evaluate SCA and FI resistant designs with significant time savings. The vulnerabilities uncovered by these approaches highlight the importance of pre-silicon analysis at each design stage to produce secure application-specific integrated circuits (ASICs).

Since finite state machines (FSMs) regulate the control flow in circuits, a computing system’s security might be breached by attacking the FSM. Physical attacks are especially worrisome because they can bypass software countermeasures. For example, an attacker can gain illegal access to the sensitive states of an FSM through fault injection, leading to privilege escalation and/or information leakage. Laser fault injection (LFI) provides one of the most effective attack vectors by enabling adversaries to precisely overturn single flip-flops states. Although conventional error correction/detection methodologies have been employed to improve FSM resiliency, their substantial overhead makes them unattractive to circuit designers. In our prior work, a novel decision diagram-based FSM encoding scheme called PATRON was proposed to resist LFI according to attack parameters, e.g., number of simultaneous faults. Although PATRON bested traditional encodings keeping overhead minimum, it provided numerous candidates for FSM designs requiring exhaustive and manual effort to select one optimum candidate. In this article, we automatically select an optimum candidate by enhancing PATRON using linear programming (LP). First, we exploit the proportionality between dynamic power dissipation and switching activity in digital CMOS circuits. Thus, our LP objective minimizes the number of FSM bit switches per transition, for comparatively lower switching activity and hence total power consumption. Second, additional LP constraints along with incorporating the original PATRON rules, systematically enforce bidirectionality to at least two state elements per FSM transition. This bestows protection against different types of fault injection, which we capture with a new unidirectional metric. Enhanced PATRON (EP) achieves superior security at lower power consumption in average compared to PATRON, error-coding, and traditional FSM encoding on five popular benchmarks.

Electric Network Frequency (ENF) fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The fluctuations in ENF remain consistent across the entire power grid including when measured at physically distant geographical locations. The light intensity from such indoor lighting as fluorescent lamps and incandescent bulbs, which are connected to the power mains, varies in accordance with the ENF, and the fluctuations can be recorded using visual sensors. In this paper, mechanisms using optical sensors and video cameras to record and validate the presence of ENF fluctuations in indoor lighting are presented. Spectrogram and subspace-based signal processing techniques are applied to such recordings to extract the ENF signal by estimating its instantaneous frequencies as a function of time. A high correlation is observed between the ENF fluctuations obtained from indoor lighting and that of the ENF signal captured directly from the power mains supply. A similar mechanism is then used to demonstrate the presence of ENF signals in video recordings taken in different geographical areas. Experimental results show that ENF signals are present in visual recordings made in different geographical areas and can be used as a natural timestamp for optical sensor recordings and video surveillance recordings conducted in indoor lighting environments. Robustness of ENF fluctuation traces under strong compression and CMOS rolling shutter cameras is discussed. Applications of the ENF signal analysis to tampering detection of surveillance video recordings and forensic binding of the audio and visual track of a video are also demonstrated. An analytical model based on an autoregressive process is also developed for ENF signals, and the effectiveness of using innovation sequences from the model for timestamp verification is demonstrated.

Localization of constituent nodes is of fundamental importance in many wireless sensor networks (WSNs) related applications. Existing research has mainly investigated the problem of localization in static WSNs, where the localization is performed mainly at the time of the node deployment. In contrast, it is important to keep track of the current locations of the nodes by invoking the localization algorithm periodically in mobile nodes. The high computation cost associated with most existing localization algorithms makes them less practical to use in resource constrained mobile sensor networks (MSNs). Additionally, these existing techniques often fail in hostile environments where some of the nodes may be compromised by adversaries, and used to transmit misleading information aimed at preventing accurate localization of the remaining sensors. In this paper, we build on our earlier work to propose an iterative gradient descent based technique with low computational complexity to securely localize nodes in MSNs. The proposed algorithm combines iterative gradient descent with selective pruning of inconsistent measurements to achieve a high localization accuracy. Simulation results demonstrate that the proposed algorithm can find a map of relative locations of the MSN even when some nodes are compromised and transmit false information.

Many applications of wireless sensor networks require precise knowledge of the locations of constituent nodes. In these applications, it is desirable for the nodes to be able to autonomously determine their locations before they start sensing and transmitting data. Most localization algorithms use anchor nodes with known locations to determine the positions of the remaining nodes. However, these existing techniques often fail in hostile environments where some of the nodes may be compromised by adversaries and used to transmit misleading information aimed at preventing accurate localization of the remaining sensors. In this paper, a computationally efficient secure localization algorithm that withstands such attacks is described. The proposed algorithm combines iterative gradient descent with selective pruning of inconsistent measurements to achieve high localization accuracy. Results show that the proposed algorithm utilizes fewer computational resources and achieves an accuracy better than or comparable to that of existing schemes. The proposed secure localization algorithm can also be used in mobile sensor networks, where all nodes are moving, to estimate the relative locations of the nodes without relying on anchor nodes. Simulations demonstrate that the proposed algorithm can find the relative location map of the entire mobile sensor network even when some nodes are compromised and transmit false information.

Secure signal processing is an emerging technology to enable signal processing tasks in a secure and privacy-preserving fashion. It has attracted a great amount of research attention due to the increasing demand to enable rich functionalities for private data stored online. Desirable functionalities may include search, analysis, clustering, etc. In this paper, we discuss the research issues and challenges in secure video processing with focus on the application of secure online video management. Video is different from text due to its large data volume and rich content diversity. To be practical, secure video processing requires efficient solutions that may involve a trade-off between security and complexity. We look at three representative video processing tasks and review existing techniques that can be applied. Many of the tasks do not have efficient solutions yet, and we discuss the challenges and research questions that need to be addressed.

Previous analysis of content fingerprints has mainly focused on the case of independent and identically distributed finger prints. Practical fingerprints, however, exhibit correlations between components computed from successive frames. In this paper, a Markov chain based model is used to capture the temporal correlations, and the suitability of this model is evaluated through experiments on a video database. The results indicate that the Markov chain model is a good fit only in a certain regime. A hybrid model is then developed to account for this behavior and a corresponding adaptive detector is derived. The adaptive detector achieves better identification accuracy at a small computational expense.

Electric Network Frequency (ENF) fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The fluctuations in the ENF remain consistent across the entire power grid even when measured at physically distant locations. The near-invisible flickering of fluorescent lights connected to the power mains reflect these fluctuations present in the ENF. In this paper, mechanisms using optical sensors and video cameras to record and validate the presence of the ENF fluctuations in fluorescent lighting are presented. Signal processing techniques are applied to demonstrate a high correlation between the fluctuations in the ENF signal captured from fluorescent lighting and the ENF signal captured directly from power mains supply. The proposed technique is then used to demonstrate the presence of the ENF signal in video recordings taken in various geographical areas. Experimental results show that the ENF signal can be used as a natural timestamp for optical sensor recordings and video surveillance recordings from indoor environments under fluorescent lighting. Application of the ENF signal analysis to tampering detection of surveillance video recordings is also demonstrated.

Multimedia identification via content fingerprints is used in many applications, such as content filtering on user-generated content websites, and automatic multimedia identification and tagging. A compact “fingerprint” is computed for each multimedia signal that captures robust and unique properties of the perceptual content, which is later used for identifying the multimedia. Several different multimedia fingerprinting schemes have been proposed in the literature and have been evaluated through experiments. To complement these experimental evaluations and provide guidelines for choosing system parameters and designing better schemes, this paper develops models for content fingerprinting and provides an analysis of the identification performance under these models. As a first step, bounds on the identification accuracy and the required fingerprint length for the simplest case when the fingerprint bits are modeled as i.i.d. are summarized. Markov Random Fields are then used to address more realistic settings of fingerprints with correlated components. The optimal likelihood ratio detector is derived and a statistical physics inspired approach for computing the probability of detection and probability of false alarm is described. The analysis shows that the commonly used Hamming distance detection criterion is susceptible to correlations among fingerprint bits, whereas the optimal log-likelihood ratio decision rule yields 5-20% improvement in the accuracy over a range of correlations. Simulation results demonstrate the validity of the theoretical predictions.

Many sensor network related applications require precise knowledge of the location of constituent nodes. In these applications, it is desirable for the wireless nodes to be able to autonomously determine their locations before they start sensing and transmitting data. Most localization algorithms rely on anchor nodes whose locations are known to determine the positions of the remaining nodes. In an adversarial scenario, some of these anchor nodes could be compromised and used to transmit misleading information aimed at preventing the accurate localization of the remaining sensors. In this paper, a computationally efficient algorithm to determine the location of sensors that can resist such attacks is described. The proposed algorithm combines gradient descent with a selective pruning of inconsistent measurements to achieve good localization accuracy. Simulation results show that the proposed algorithm has performance comparable to existing schemes while requiring less computational resources.

Content fingerprinting provides a compact representation of multimedia objects for copy identification. This paper analyzes the impact of the ordinal-ranking based feature encoding on the performance of content fingerprinting. Expressions are derived for the identification performance of a fingerprinting system with and without ordinal ranking. The analysis indicates that when the number of features is moderately large, ordinal ranking can improve the robustness of the fingerprinting system to large distortions of the features and significantly increase the probability of detection. These results enhance understandings of ordinal ranking and provide design guidelines for choosing different system parameters to achieve a desired identification accuracy.

With the increasing popularity of digital multimedia such as images and videos and the advent of the cloud computing paradigm, a fast growing amount of private and sensitive multimedia data are being stored and managed over the network cloud. To provide enhanced security and privacy protection beyond traditional access control techniques, privacy preserving multimedia retrieval techniques have been proposed recently to allow content-based multimedia retrieval directly over encrypted databases and achieve accurate retrieval comparable to conventional retrieval schemes. In this paper, we introduce a security definition for the privacy preserving retrieval scenario and show that the recently proposed schemes are secure under the proposed security definition.

This paper addresses the problem of image retrieval from an encrypted database, where data confidentiality is preserved both in the storage and retrieval process. The paper focuses on image feature protection techniques which enable similarity comparison among protected features. By utilizing both signal processing and cryptographic techniques, three schemes are investigated and compared, including bit-plane randomization, random projection, and randomized unary encoding. Experimental results show that secure image retrieval can achieve comparable retrieval performance to conventional image retrieval techniques without revealing information about image content. This work enriches the area of secure information retrieval and can find applications in secure online services for images and videos.

This paper describes a method for hiding data inside printed text documents that is resilient to print/scan and photocopying operations. Using the principle of channel coding with side information, the embedder inserts a message into a text document while treating the content of the document as known interference. The data is embedded by making small changes to text characters before the document is printed. Using a simple correlation-based detector in conjunction with an error correction code, the hidden data can be extracted from a photocopy of the printed document. By enhancing the detector with an optical character recognition algorithm, the embedded data can be extracted even after multiple rounds of photocopying. Results from subjective tests show that the changes made by the embedding algorithm, while perceptible, are not obtrusive to a lay reader.

Content fingerprinting provides a compact content-based representation of a multimedia document. An important application of fingerprinting is the identification of modified copies of the original media content. These modifications may be incidental changes that occur during the usage of multimedia, or intentional modifications made by an adversary to avoid detection. Currently, the effectiveness of content identification techniques is often assessed through benchmark databases. To complement these experimental performance evaluations, this paper develops a theoretical framework for analyzing content identification techniques. Beneficial aspects from decision theory and game theory are exploited to gain insights toward optimal system design and parameter selection.

Digital fingerprinting is a technique to deter unauthorized redistribution of multimedia content by embedding a unique identifying signal in each legally distributed copy. The embedded fingerprint can later be extracted and used to trace the originator of an unauthorized copy. A group of users may collude and attempt to create a version of the content that cannot be traced back to any of them. As multimedia data is commonly stored in compressed form, this paper addresses the problem of fingerprinting compressed signals. Analysis is carried out to show that due to the quantized nature of the host signal and the embedded fingerprint, directly extending traditional fingerprinting techniques for uncompressed signals to the compressed case leads to low collusion resistance. To overcome this problem and improve the collusion resistance, a new technique for fingerprinting compressed signals called Anti-Collusion Dither (ACD) is proposed, whereby a random dither signal is added to the compressed host before embedding so as to make the effective host signal appear more continuous. The proposed technique is shown to reduce the accuracy with which attackers can estimate the host signal, and from an information theoretic perspective, the proposed ACD technique increases the maximum number of users that can be supported by the fingerprinting system under a given attack. Both analytical and experimental studies confirm that the proposed technique increases the probability of identifying a guilty user and can approximately quadruple the collusion resistance compared to conventional Gaussian fingerprinting.

Content identification has many applications, ranging from preventing illegal sharing of copyrighted content on video sharing websites, to automatic identification and tagging of content. Several content identification techniques based on watermarking or robust hashes have been proposed in the literature, but they have mostly been evaluated through experiments. This paper analyzes binary hash-based content identification schemes under a decision theoretic framework and presents a lower bound on the length of the hash required to correctly identify multimedia content that may have undergone modifications. A practical scheme for content identification is evaluated under the proposed framework. The results obtained through experiments agree very well with the performance suggested by the theoretical analysis.

Nested or concatenated codes are often used for traitor tracing schemes as they require a small symbol size to accommodate a given number of users. Typically, tracing is performed on each layer of the code separately - tracing is first performed on the inner code and the information obtained is subsequently used to perform tracing on the outer code. In such situations, the collusion resistance is determined by the minimum of the coalition sizes that can be tolerated by the individual codes. Due to the small symbol size, the inner codes can tolerate a smaller number of colluders resulting in a small overall collusion resistance. Further, recovering more attacked versions does not enable identification of larger coalitions. To improve the collusion resistance, in this paper we propose to pass soft information from the inner code tracing to the outer code tracing. We demonstrate through simulations and formal analysis that the proposed technique improves the collusion resistance of tracing systems employing nested codes.

Most existing collusion-resistant fingerprinting techniques are for fingerprinting uncompressed signals. In this paper, we first study the performance of the traditional Gaussian based spread spectrum sequences for fingerprinting compressed signals and show that the system can be easily defeated by averaging or taking the median of a few copies. To overcome the collusion problem for compressed multimedia host signals, we propose a technique called anti-collusion dithering to mimic an uncompressed signal. Results show higher probability of catching a colluder using the proposed scheme compared to using Gaussian based fingerprints.

In this paper, we analyze the effect of various collusion attacks on fingerprinting systems for compressed multimedia. We evaluate the effectiveness of the collusion attacks in terms of the probability of detection and accuracy in estimating the host signal. Our analysis shows that applying averaging collusion on copies of moderately compressed content gives a highly accurate estimation of the host, and can effectively remove the embedded fingerprints. Averaging is thus the best choice for an attacker as the probability of detection and the distortion introduced are the lowest.