Source Authentication Research Articles

Differentiation and quantification of bovine and pork gelatin using UPLC-QTOF and ATR-FTIR spectroscopy: Addressing challenges in mixed gelatin analysis and detection

The amino acid sequence dictates protein folding and spatial structure, essential for understanding their behavior. Gelatin, important in industry and biomedicine, demands accurate source authentication due to ethical and dietary concerns. This study explores differentiating and quantifying bovine and pork gelatin using UPLC-QTOF and ATR-FTIR spectroscopy. UPLC-QTOF identified distinct ions for pure bovine (m/z + 962.1471, NRLHFFK) and pork gelatin (m/z + 653.0289, YNEVK) but struggled with mixed gelatins due to protein-protein interactions altering peptide ion profiles. ATR-FTIR was employed to leverage ethanol's differential effects on gelatin's amide bands for quantifying pork gelatin contamination in bovine gelatin. The method showed a strong linear correlation (R2 = 0.9994) with contamination levels and amide band transmission, with detection and quantification limits of 0.85 and 2.85 mg/100 mg, respectively. It effectively identified pork gelatin in halal candy, with recovery rates from 50.05 % to 103.69 %, highlighting ATR-FTIR's potential for accurate gelatin analysis.

Optical image authentication and encryption scheme with computational ghost imaging

Most of the previous image encryption schemes overlook certifying the source of the encrypted images, thus the receiver may be misled by messages coming from unauthorized users or wrong sources. To simultaneously accomplish image encryption and source authentication of the encrypted images, an optical image authentication and encryption scheme is proposed by integrating computational ghost imaging (CGI) with quick response (QR) code. Firstly, the plaintext image is converted into a QR code and encrypted by a scrambling-diffusion structure. Then, the authentication image is enciphered by a CGI system and a diffusion structure. The intermediate resulting images are marked and embedded into the random matrices to withstand the differential attack. Moreover, the source of the encrypted images can be identified by the reconstructed authentication image and the nonlinear correlation coefficient (NCC) result. The authentication is considered successful if a sharp peak appears in the center of the NCC result. Numerical simulations show that the proposed scheme can not only withstand different attacks, but also avoid being misled by misinformation during the decryption process.

Smart agriculture assurance: IoT and blockchain for trusted sustainable produce

Current conventional agricultural practices have exacerbated the depletion of soil, loss of biodiversity, and intensified the substantial challenges of food security including monoculture dependence, resource intensiveness, access and distribution issues. This has raised consumers awareness towards monitoring the resources of their food consumption. Nevertheless, greenwashing and deceptive food labels are prevalent leading to sales of items with misaligned labels, underpinning the need to authenticate, trace, and track the agricultural supply chain from the farm to shop. Consumers should be able to select fresh produce and verify its origin through a transparent, trustworthy, accountable, and reliable process. This paper introduces a blockchain-based solution that leverages intrinsic features of this innovative and disruptive technology to harness transparency, reliability, authenticity, and accountability within an agricultural supply chain. Additionally, our solution eliminates misleading food labels on fresh produce by enabling consumers to reliably trace and verify the origin and authenticity of the food source in a transparent manner. Through the integration of the Internet of Things (IoT), the well-being of the soil, air, and crops is consistently monitored. The sustainable solutions employed are transparent, aligning with the standards set by various sustainable food certifications and agencies, including organic, non-Genetically Modified Organisms (GMO), and Good Agricultural Practices (GAP). The paper presents detailed algorithms that represent the smart contract code, sequence diagrams along with testing results and security analysis. The smart contract code is made publicly available on GitHub.

Fake News Detection using Machine Learning

Fake News Detection using Machine Learning

Neurological Diagnosis: Artificial Intelligence Compared With Diagnostic Generator.

Artificial intelligence has recently become available for widespread use in medicine, including the interpretation of digitized information, big data for tracking disease trends and patterns, and clinical diagnosis. Comparative studies and expert opinion support the validity of imaging and data analysis, yet similar validation is lacking in clinical diagnosis. Artificial intelligence programs are here compared with a diagnostic generator program in clinical neurology. Using 4 nonrandomly selected case records from New England Journal of Medicine clinicopathologic conferences from 2017 to 2022, 2 artificial intelligence programs (ChatGPT-4 and GLASS AI) were compared with a neurological diagnostic generator program (NeurologicDx.com) for diagnostic capability and accuracy and source authentication. Compared with NeurologicDx.com, the 2 AI programs showed results varying with order of key term entry and with repeat querying. The diagnostic generator yielded more differential diagnostic entities, with correct diagnoses in 4 of 4 test cases versus 0 of 4 for ChatGPT-4 and 1 of 4 for GLASS AI, respectively, and with authentication of diagnostic entities compared with the AI programs. The diagnostic generator NeurologicDx yielded a more robust and reproducible differential diagnostic list with higher diagnostic accuracy and associated authentication compared with artificial intelligence programs.

EPri-MDAS: An efficient privacy-preserving multiple data aggregation scheme without trusted authority for fog-based smart grid

With the increasingly pervasive deployment of fog servers, fog computing extends data processing and analysis to network edges. At the same time, as the next-generation power grid, the smart grid should meet the requirements of security, efficiency, and real-time monitoring of user energy consumption. By utilizing the low-latency and distributed properties of fog computing, it can improve communication efficiency and user service satisfaction in smart grids. For the sake of providing adequate functionality for the power grid, various schemes have been proposed. Whereas, many methods are vulnerable to privacy leakage since the existence of trusted authority may increase the exposure to threats. In this paper, we propose the EPri-MDAS: an Efficient Privacy-preserving Multiple Data Aggregation Scheme without trusted authority based on the ElGamal homomorphic cryptosystem, which achieves both data integrity verification and data source authentication with the most efficient block cipher-based authenticated encryption algorithm. It performs well in energy efficiency with strong security. Especially, the proposed multidimensional aggregation statistics scheme can perform the fine-grained data analyses; it also allows for fault tolerance while protecting personal privacy. The security analysis and simulation experiments show that EPri-MDAS can satisfy the security requirements and work efficiently in the smart grid.

TokenDoc: Source Authentication With a Hybrid Approach of Smart Contract and RNN-Based Models With AES Encryption

TokenDoc: Source Authentication With a Hybrid Approach of Smart Contract and RNN-Based Models With AES Encryption

Compliance Source Authentication Technique for Person Adaptation Networks Utilizing Deep Learning-Based Patterns Segmentation

Compliance Source Authentication Technique for Person Adaptation Networks Utilizing Deep Learning-Based Patterns Segmentation

L-TCM: A Lightweight Privacy-Preserving Traffic Condition Monitoring Scheme With Source Authentication in Cloud-Assisted VANETs

In vehicular ad hoc networks (VANETs), the vehicle constantly collects traffic condition messages and uploads them to the traffic management department (TMD) for traffic analysis. Traffic condition messages usually consist of vehicle-related information as well as safety-related messages. Vehicle-related information usually contains vehicles' privacy, which cannot be broadcasted. At the same time, the safety-related message does not contain the privacy of vehicles and can be broadcasted in VANETs to ease traffic congestion. However, transmitting two types of messages separately consumes numerous computation and communication costs, which is not realistic for resource-limited vehicles. In this article, we propose a lightweight privacy-preserving traffic condition monitoring (L-TCM) scheme, where the sanitizable signature is used to enable the vehicle to broadcast safety-related messages and upload traffic condition messages by sending one message. In addition, searchable encryption is used to help the TMD retrieve traffic condition messages stored in the cloud server. Security analysis demonstrates that L-TCM satisfies security goals in VANETs. Besides, the performance of L-TCM concerning computation and communication costs makes it appropriate for traffic condition monitoring.

Watermarking approach for source authentication of web content in online social media: a systematic literature review

Watermarking approach for source authentication of web content in online social media: a systematic literature review

PF-IBDA: Provably secure and pairing-free identity-based deniable authentication protocol for MANET environments

Mobile Adhoc Network (MANET) is used in various real-time applications, such as e-voting, army tactical communication, health-care applications, disaster rescue, online message exchange, etc. However, source authentication and deniability are essential properties in such applications. Typically, these properties can be achieved using a deniable authentication (DA) protocol. With the help of a DA protocol, a mobile receiver node can directly verify the source, another mobile node, of a message without consulting a trusted third party (TTP). Recently, many identity-based deniable authentication (IBDA) protocols have been proposed. Most of these protocols are insecure and computationally expensive since they use costly operations, such as bilinear pairing and map-to-point hash function. We proposed a pairing-free identity-based deniable authentication (PF-IBDA) protocol for MANET environments. We proved that the PF-IBDA protocol could provide indistinguishability against adaptive chosen ciphertext attack (IND-CCA2) in the random oracle model (ROM) based on the hardness assumption of the elliptic curve computational Diffie–Hellman (ECCDH) problem. We have computed the execution time of PF-IBDA protocol in different security levels: 80-bit, 112-bit, 128-bit, 192-bit, and 256-bit on a mobile device using the JPBC library and compared it with the state-of-the-art IBDA protocols. We found that the proposed PF-IBDA protocol is more efficient than the existing IBDA protocols.

Fake news detection using a deep learning transformer based encoder-decoder architecture

Fake news production, accessibility, and consumption have all increased with the rise of internet-connected gadgets and social media platforms. A good fake news detection system is essential because the news readers receive can affect their opinions. Several works on fake news detection have been done using machine learning and deep learning approaches. Recently, the deep learning approach has been preferred over machine learning because of its ability to comprehend the intricacies of textual data. The introduction of transformer architecture changed the NLP paradigm and distinguished itself from recurrent models by enabling the processing of sentences as a whole rather than word by word. The attention mechanisms introduced in Transformers allowed them to understand the relationship between far-apart tokens in a sentence. Numerous deep learning works on fake news detection have been published by focusing on different features to determine the authenticity of a news source. We performed an extensive analysis of the comprehensive NELA-GT 2020 dataset, which revealed that the title and content of a news source contain discernible information critical for determining its integrity. To this objective, we introduce ‘FakeNews Transformer’ — a specialized Transformer-based architecture that considers the news story’s title and content to assess its veracity. Our proposed work achieved an accuracy of 74.0% on a subset of the NELA-GT 2020 dataset. To our knowledge, FakeNews Transformer is the first published work that considers both title and content for evaluating a news article; thus, we compare the performance of our work against two BERT and two LSTM models working independently on title and content. Our work outperformed the BERT and LSTM models working independently on title by 7.6% and 9.6%, while performing better than the BERT and LSTM models working independently on content by 8.9% and 10.5%, respectively.

How does herd behaviour impact the purchase intention? Explore the moderating effect of risk aversion in the context of Vietnamese consumers

Managers have been applying the influence of herd behaviour to stimulate purchase intention but have not achieved precise results because they do not clearly understand the mechanism of this relationship. This article aims to determine the effect of herd behaviour on the relationship between information adoption and purchase intention and the moderating of risk aversion. With 17 variables for four concepts, data was collected online with 340 respondents and analysed with a partial least squares structural equation modelling. When considering herd behaviour in terms of subjective norms with corroborated information, this study upgrades the classic role of the theory of planned behaviour in purchase intention. The findings indicate a positive relationship between herd behaviour, information adoption and purchase intention. In particular, risk aversion is a positive moderator of the relationship between herd behaviour and purchase intention. This result is a significant addition to the theoretical body and shows that risk aversion can drive purchase intention through herd behaviour. In other words, this study upgrades the perception of information source authentication through herd behaviour to explain the purchase intentions of Vietnamese consumers. These results show that managers can create risky situations to promote herd behaviour. They can also apply information adoption stimuli to stimulate herd behaviour. At the same time, through the information adoption mechanism, consumers can be more alert to the effect of herd behaviour and have more opinions when making purchases.

Lightweight and Verifiable Secure Aggregation for Multi-dimensional Data in Edge-enhanced IoT

The development of IoT applications promotes the generation of large-scale data. In this situation, more and more organizations or institutions are interested in collecting and analyzing these data to obtain high-quality information to serve people’s lives. Nevertheless, existing secure aggregation solutions are mostly proposed under the honest-but-curious threat model, without considering malicious aggregators. Actually, the aggregation node may return tampered or forged aggregation results for vulnerabilities or ulterior motives. It is more challenging to devise efficient verifiable data aggregation due to the characteristics of IoT devices. In this paper, we propose a Lightweight and Verifiable Secure Aggregation Scheme for Multi-dimensional Data in Edge-enhanced IoT (LVSA-MD), which allows the secure aggregation of multi-dimensional data, and the requester can verify the correctness and integrity of the aggregation results. In LVSA-MD, we combine Chinese remainder theorem with secret sharing technique to protect the individual data privacy, as well as reducing the communication overhead. Moreover, by involving homomorphic MAC, we achieve a lightweight verifiable mechanism to ensure the integrity of result. We additionally employ identity-based signature to achieve the source authentication. Detailed security analysis demonstrates that the proposed LVSA-MD achieves the goal of protecting privacy, integrity and authentication. Moreover, extensive theoretical analyses and experimental evaluations show that our LVSA-MD performs efficiently with respect to computation and communication while retaining more desired properties.

Blockchain-Based Decentralized and Lightweight Anonymous Authentication for Federated Learning

Federated learning (FL) is a promising technology for achieving privacy-preserving edge intelligence and has attracted extensive attention from industry and academia. However, in the FL training process, the server directly aggregates local models from mobile devices, which poses serious privacy and security threats. The identity authentication mechanism can provide FL with local model integrity and source authentication. However, the existing schemes are centralized, and most of them are computationally expensive, resulting in limited performance. To address these issues, this paper proposes a decentralized and lightweight anonymous FL identity authentication scheme, namely DAFL. In our scheme, we first design a decentralized and simplified storage FL authentication framework by combining the directed acyclic graph (DAG) blockchain and accumulator. Then, we propose a lightweight digital signature algorithm that supports batch verification for authentication. Finally, nodes interact through pseudonyms to achieve anonymous communication, and the trusted authority (TA) can track and recover the real identities of nodes when malicious behavior occurs. We theoretically prove the security of the proposed DAFL. The extensive experiments demonstrate that DAFL achieves lower authentication overhead and better convergence performance compared to existing authentication schemes and vanilla FL systems.

MSA-SDMN: multicast source authentication scheme for multi-domain software defined mobile networks

ABSTRACT Multicast services provide an efficient way of conserving resources and reducing network traffic for multicast senders. However, ensuring security, particularly source authentication, is crucial for applications such as online news, IPTV, video streaming, and stock quote distribution. Previous research efforts have attempted to provide source authentication for multicast applications, but they often struggle to handle the dynamic nature of multicast mobile receivers and multi-domain networks. This paper provides an analysis of research conducted over 22 years on source authentication mechanisms with non-repudiation. Most of these mechanisms fail to address the dynamic nature of mobile users and multi-domain networks. To address this issue, a new multicast source authentication scheme for multi-domain Software Defined Mobile Networks (SDMN) is proposed. This approach uses the global view of the controller in SDMN to operate in dynamic environments, provide non-repudiation, and tolerate packet loss. Simulation results indicate that the proposed mechanism uses resources efficiently, reduces communication and delay overhead, and performs well in multi-domain and dynamic networks.

Blockchain-Based Authentication of GNSS Civil Navigation Message

The Civil Navigation Message (CNAV) of the Global Navigation Satellite System (GNSS) is transmitted in an open channel without authentication and integrity protection mechanism. Therefore, the CNAV is facing the threat of interception and tampering, and it is vulnerable to spoofing attacks. To guarantee the authenticity and integrity of CNAV, we propose a cryptographic anti-spoofing scheme of CNAV in GNSS based on consortium blockchain using domestic cryptographic algorithms. In this scheme, we design a CNAV authentication model based on satellites, ground stations and key management center. And implement the authentication protocol to achieve the purpose of preventing CNAV in GNSS from being tampered with. On the basis of the authentication of the entities, the scheme further realizes the functions of information source authentication and integrity protection of the legitimate senders. And on the basis of satisfying security, the protocol has lower computing and communication cost, simplifies the process of re-authentication of information source, and has great practicability for receivers that use civil navigation message to achieve navigation and positioning functions.

IMGCAT: An approach to dismantle the anonymity of a source camera using correlative features and an integrated 1D convolutional neural network

With the proliferation of smartphones, digital data collection has become trivial. The ability to analyze images has increased, but source authentication has stagnated. Editing and tampering of images has become more common with advancements in signal processing technology. Recent developments have introduced the use of seam carving (insertion and deletion) techniques to disguise the identity of the camera, specifically in the child pornography market. In this article, we focus on the available features in the image based on PRNU (photo response nonuniformity). The forced-seam sculpting technique is a well-known method to create occlusion for camera attribution by injecting seams into each 50 × 50 pixel block. To counter this, we perform camera identification using a 1D CNN integrated with feature extractions on 20 × 20 pixel blocks. We achieve state-of-the-art performance for our proposed IMGCAT (image categorization) in three-class classification over the baselines (original, seam removed, seam inserted). Based on our experimental findings, our model is robust when dealing with blind facts related to the questionable camera.

Blockchain-based fake news traceability and verification mechanism

The rapid development of the Internet and Internet of Things has rapidly introduced human society into the information age, and the way of fake news production has been updated, which has greatly affected the normal life of human beings. In order to identify worthless fake news and trace massive fake news data from unknown sources, and share valuable news data to fully disseminate effective real news, news owners usually store news data in cloud. Users of IoT terminals can access news data on demand without storing it locally. However, the authenticity of the fictive newspaper numbers source, which is easy to destroy, and the social media platform. Besides, when massive news data is saved on cloud server, the news owners have to at the risk of lose physical control over news data and it will face the risk of fake news being disseminated and real news being falsified. Thus, this paper proposes a novel mechanism for secure storage of news data using blockchain technology. Firstly, traceability and verification of fake news data is improved by the cooperative storage model on and off the chain. Secondly due to the inability of past polynomial commitment to update the commitment, we will be a hindrance to use polynomial commitment to build a secure authentication protocol. Therefore, in this paper, we design the update algorithm for polynomial commitment in order to be able to guarantee the consistency of on-chain and blockchain database news data.

A Data Reporting Protocol With Revocable Anonymous Authentication for Edge-Assisted Intelligent Transport Systems

Intelligent Transport Systems (ITS) have received growing attention recently driven by technical advances in Industrial Internet of Vehicles (IIoV). In IIoV, vehicles report traffic data to management infrastructures so as to achieve better ITS services. To ensure both security and privacy, many anonymous authentication-enabled data reporting protocols have been proposed. However, these protocols usually require a large number of preloaded pseudonyms or involve a costly and irrevocable group signature. Thus, they are not ready for realistic deployment due to large storage overhead, expensive computation costs, or absence of malicious users' revocation. To address these issues, we present a novel data reporting protocol for edge-assisted ITS in this paper, where the traffic data is sent to the distributed edge nodes for local processing. In particular, we propose a new anonymous authentication scheme fine-tuned to fulfill the needs of vehicular data reporting, which allows the authenticated vehicles to report unlimited unlinkable messages to edge nodes without huge pseudonyms download and storage costs. Moreover, we designed an efficient certificate update scheme based on a bivariate polynomial function. In this way, the malicious vehicles can be revoked with time complexity O(1). The security analysis demonstrates that our protocol satisfies source authentication, anonymity, unlinkability, traceability, revocability, non-frameability, and non-repudiation. Further, extensive simulation results show that the efficiency and performance of our protocol are greatly improved since the signature size is reduced by at least 8%, the computation costs in message signing and verification are reduced by at least 56% and 67%, respectively, and the packet loss rate is reduced by at least 14%.