Secure digital image watermarking using memristor-based hyperchaotic circuit

Sonam, -; Sehra, Khushwant; Singh, Raghvendra Pratap; Singh, Sharat; Wadhera, Shweta; Kasturi, Poonam; Saxena, Geetika Jain; Saxena, Manoj

doi:10.1007/s00371-022-02601-3

Cited by 6 publications

(1 citation statement)

References 51 publications

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“…Watermarking has been widely implemented with image-based media. Although there are a few image watermarking methods applied to memristors as presented in [63], there has not been extensive research performed on watermarking memristor weights, in general, especially those used in an RF-based context.…”

Section: Watermarking and Stenographymentioning

confidence: 99%

Survey of Security Issues in Memristor-Based Machine Learning Accelerators for RF Analysis

Lillis,

Hoffing,

Burleson

2024

Chips

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We explore security aspects of a new computing paradigm that combines novel memristors and traditional Complimentary Metal Oxide Semiconductor (CMOS) to construct a highly efficient analog and/or digital fabric that is especially well-suited to Machine Learning (ML) inference processors for Radio Frequency (RF) signals. Analog and/or hybrid hardware designed for such application areas follows different constraints from that of traditional CMOS. This paradigm shift allows for enhanced capabilities but also introduces novel attack surfaces. Memristors have different properties than traditional CMOS which can potentially be exploited by attackers. In addition, the mixed signal approximate computing model has different vulnerabilities than traditional digital implementations. However both the memristor and the ML computation can be leveraged to create security mechanisms and countermeasures ranging from lightweight cryptography, identifiers (e.g., Physically Unclonable Functions (PUFs), fingerprints, and watermarks), entropy sources, hardware obfuscation and leakage/attack detection methods. Three different threat models are proposed: (1) Supply Chain, (2) Physical Attacks, and (3) Remote Attacks. For each threat model, potential vulnerabilities and defenses are identified. This survey reviews a variety of recent work from the hardware and ML security literature and proposes open problems for both attack and defense. The survey emphasizes the growing area of RF signal analysis and identification in terms of commercial space, as well as military applications and threat models. We differ from other recent surveys that target ML, in general, neglecting RF applications.

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Section: Watermarking and Stenographymentioning

confidence: 99%

Survey of Security Issues in Memristor-Based Machine Learning Accelerators for RF Analysis

Lillis,

Hoffing,

Burleson

2024

Chips

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Pavlov dog feature memory circuit based on memristor under different emotional conditions

Wang,

Yang,

Xin

et al. 2024

Circuit Theory & Apps

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The memristor‐based Pavlov circuits have been extensively studied, but most of the circuits only have learning functions plus forgetting function, and lack emotional function and feature memory. This leads to not be realistic enough for simulating Pavlovian circuit experiments. In this paper, we simulate learning and three forgetting processes on the classic Pavlov circuit, which is consistent with the brain's forgetting mechanism. Then, we add feature memory part, which includes an emotion module, synapse modules, and feature switch modules. The emotion module with positive and negative emotion functions has different effects on memory time, so as to improve its imitating ability of human emotional behavior. The synapse modules and feature switching modules realize different feature memory for different things, so as to promote the circuit's associative memory ability to be more realistic. Through these improvements, the function of the Pavlov dog experiment circuit is more similar to biological mechanism. Furthermore, the LTSPICE software is used to verify the correctness of the proposed circuit.

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