Integrity Verification Research Articles

Semi-Blind watermarking using convolutional attention-based turtle shell matrix for tamper detection and recovery of medical images

“Telemedicine or e-medicine” refers to an electronic communication system that shares medical information from one location to another through the transmission channel. The increase in multimedia data sharing/transmission across the communication channel has raised to an uncontrollable stage. Thus, the transmission of medical reports across the network has increased higher, which has a chance of tampering either intentional or unintentional attacks. In the case of deliberate attacks, tamper detection and localization are the highly required criteria to verify the originality. In such cases, medical image authentication, security, and integrity verification are also essential criteria in telemedicine applications to verify whether the report belongs to the right person or not. To achieve these criteria and to satisfy these requirements Digital watermarking techniques are highly suggested. Much research has been carried out to address this issue, but some drawbacks and limitations still need to be addressed. The objective is to accomplish a Tamper detection, localization, and recovery (TDLR) system with minimal source information at the receiver end with a better payload or embedding capacity. This paper presents a novel Conventional Attention network (CoAtNet) based invariant watermark generation. A two-level embedding is proposed to enhance watermark security, where image tamper localization and recovery information is generated. The primary level of embedding is carried out by the Turtle Shell Data Hiding Algorithm (TSDH), and the secondary level of embedding is carried out by the DWT-SVD transform. The result shows that the proposed model achieves high imperceptibility and tamper detection accuracy of 60.24 and 99.51% respectively. Furthermore, the suggested system performed well in evaluations of tamper detection and image restoration for various malicious attacks against state-of-the-art systems.

Implementation of a Patient Specific QA Protocol for a Novel Dedicated Stereotactic Radiosurgery Linear Accelerator

To describe the patient specific QA (Quality Assurance) protocol implemented for plan integrity verification, in stereotactic radiosurgery (SRS) and radiotherapy (SRT) treatments performed on a novel dedicated frameless image guided radiosurgery system using conical collimator, and a combination of yoked gimbals to cover near2pi solid angle. Startup and commissioning results obtained for planned vs measured dose distributions with several detectors are presented. The patient specific QA protocol includes: A) Review of the approved plan based on AAPM TG 275 report recommendations B) 1D verification using small volume ionization chambers inside an anthropomorphic head phantom C) 2D dose verification of a coronal and sagittal dose plan performed with high resolution 2D Array and respective phantom apparatus D) In-house independent MU (Monitor Unit) calculation using the same formalism of the vendor TPS (Treatment Planning Systems) RayTracing dose-calculation algorithm E) Monte Carlo based secondary dose check & plan QA. A retrospective analysis of the results of the first 15 patients treated is presented, focusing on plan complexity vs QA results. The 1D results obtained for the 15 SRS plans were within ±5% for all reported cases, with a mean percent difference of -1,25%, confirming an overall good agreement and, as expected, a partial volume effect in plans with smaller collimators. For the 2D dose verifications, with a 10% dose threshold, gamma passing rates of 97,5% (coronal) and 96,78 (sagittal) with 3% 1mm criteria, 95,1% (coronal) and 94,3% (sagittal) for 2% 1 mm criteria and 90,7% (coronal) and 90,0% (sagittal) for 1% 1mm criteria were obtained. Moreover, the results showed a correlation between lesion volume or number of collimators used with gamma passing rates. All MU verification results were within ±0,3% and provides an efficient risk mitigation approach for this new delivery technique. The verification results of the first 15 treated SRS plans confirmed point dose and planar measurements in agreement with TPS calculations, with superior results for planes with smaller lesions and fewer collimators. This also represents an integral validation of the image-based alignment system and fine treatment couch movements, as treatments are intrinsically multi-isocentric.

Invertible Neural Network for Inference Pipeline Anomaly Detection

This study combines research in machine learning and system engineering practices to conceptualize a paradigm-enhancing trustworthiness of a machine learning inference pipeline. We explore the topic of reversibility in deep neural networks and introduce its anomaly detection capabilities to build a framework of integrity verification checkpoints across the inference pipeline of a deployed model. We leverage previous findings and principles regarding several types of autoencoders, deep generative maximumlikelihood training and invertibility of neural networks to propose an improved network architecture for anomaly detection. We hypothesize and experimentally confirm that an Invertible Neural Network (INN) trained as a convolutional autoencoder is a superior alternative naturally suited to solve that task. This remarkable INN’s ability to reconstruct data from its compressed representation and to solve inverse problems is then generalized and applied in the field of Trustworthy AI to achieve integrity verification of an inference pipeline through the concept of an INN-based Trusted Neural Network (TNN) nodes placed around the mission critical parts of the system, as well as the end-to-end outcome verification. This work aspires to enhance robustness and reliability of applications employing artificial intelligence, which are playing increasingly noticeable role in highly consequential decision-making processes across many industries and problem domains. INNs are invertible by construction and tractably trained simultaneously in both directions. This feature has untapped potential to improve the explainability of machine learning pipelines in support of their trustworthiness and is a topic of our current studies.

Provenance of specimen and data – A prerequisite for AI development in computational pathology

AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity.

Integrity verification for scientific papers: The first exploration of the text

Scientific papers, as pivotal tools for academic communication, should be articulated with clarity and precision to ensure the effective conveyance of scholarly ideas and to prevent reader confusion. Yet, many such papers conspicuously lack in-depth research, and their core content is often ambiguously presented. This pattern poses a significant impediment to the progressive evolution of science and technology. While numerous researchers have recognized this widespread challenge, a holistic theoretical or methodological solution remains elusive in the academic realm. To bridge this gap, we introduce the INTEGrity vERification (INTEGER) task. This task aids researchers in assessing the integrity of their papers by verifying the clarity of each knowledge unit. To implement this task on text, we propose a multi-task learning model that utilizes the Tucker decomposition and span-level attention mechanism to identify terms and their integrity precisely. More specifically, to provide insights into the INTEGER task and validate the effectiveness of the proposed model, we collect 8076 sentences and construct three new datasets containing various types of terms and descriptions in different domains. Extensive experimental results show that our proposed model has an average performance improvement of 1.1% F1 over the three datasets compared to a series of state-of-the-art baseline methods.

Integrity Audition of Shared Data on Cloud with User Privacy Preserving

This paper introduces a robust solution, "Secure Data Storage and Auditing," aimed at addressing contemporary challenges in data security and integrity verification within cloud environments. Through an integrated system of modules, the project achieves secure data storage through encryption and cryptographic tagging, followed by independent data integrity audits. Users' privacy is preserved as the Third-Party Auditor (TPA) verifies data without direct access to content. The project demonstrates effective data management, user privacy preservation, and real-time auditing, contributing to data security in cloud-based infrastructures. The methodology showcases the project's systematic approach to module integration, ensuring a comprehensive solution for secure data management and accountability.

BARIK: an extended Hoek–Brown-based anisotropic constitutive model for fractured crystalline rock

Abstract. The disposal of heat-generating radioactive waste in deep geologic formations is a global concern. Numerical methods play a key role in understanding and assessing the disposal scenarios of radioactive waste in deep geological repositories. However, the complexities of the thermal, hydrological, mechanical, chemical, and biological processes associated with the disposal of radioactive waste in porous and fractured materials constitute significant challenges. One of the most challenging issues in this field is the complex material behavior of fractured crystalline rock. The presence of fractures makes the rock anisotropic, nonlinear, and dependent on loading paths. Additionally, the Biot coefficient cannot be considered constant throughout the critical and subcritical fracture development regions. These factors make the development of an accurate constitutive model for fractured crystalline hard rock a critical component of any deep geological disposal project. Furthermore, to demonstrate the integrity of the containment-providing rock zone in crystalline host rock, the qualitative integrity criteria must be quantified so that numerical simulation can be performed with concrete numerical values. Part of this assessment for a crystalline host rock is a dilatancy criterion, which is currently based on the Hoek–Brown constitutive model. BARIK is the German acronym for the research project on which this paper is based. This contribution provides an overview of the development and verification of the BARIK constitutive model, an extended Hoek–Brown model for fractured crystalline hard rock that takes into account up to three fracture systems. The model enables the consideration of the matrix and joint behavior of the rock separately, with each component having unique strength characteristics and failure criteria. These criteria are formulated such that suitable consideration of the strength-reducing properties of the respective fracture systems during barrier integrity verification is possible. The BARIK model has been implemented into two computer codes, FLAC3D and MFront for OpenGeoSys, allowing for the identification and evaluation of any inaccuracies that may arise from the use of different codes. The model enables isotropic–elastic, orthotropic–elastic, isotropic–elasto-plastic, and orthotropic–elasto-plastic calculations of the matrix, making it a valuable tool for the site selection process and for the construction and long-term safety of underground repositories. Furthermore, this poster presentation will show how the constitutive model was evaluated in relation to the dilatancy criterion and how the BARIK constitutive model's suitability for conducting an integrity assessment was validated. In conclusion, the development of BARIK is a significant step forward in the understanding and modeling of the complex material behavior of fractured crystalline hard rock. This contribution will provide insights into the development and verification of this model for the safe disposal of radioactive waste.

Power data integrity verification method based on chameleon authentication tree algorithm and missing tendency value

Abstract The power system operation and control data are from a wide range of sources. The relevant data acquisition equipment is disturbed by the complex electromagnetic environment on the power system operation and control lines, resulting in data errors and affecting the application and analysis of data. Therefore, a power data integrity verification method based on chameleon authentication tree algorithm and missing trend value is proposed. Get 2D data from different sensors and place it in the space environment. After data conversion, convert heterogeneous data into the same structure, expand the scope of power data acquisition, and conduct power system operation and control node layout and integrity data acquisition; The chameleon authentication tree algorithm is used to deal with the heterogeneous information of the power data, and the true value of the data is determined in the heterogeneous conflict of the power data at the same site; Query the integrity data based on the power system operation and control positioning node, creatively calculate the missing trend value of power data, evaluate the importance of data integrity, obtain the priority of power data integrity verification, and complete the integrity verification of power data. The experimental results show that the optimal clustering number is 9.05, the distribution coefficient is 16.30, the absolute error of validity analysis is 2.80, all test indicators are close to the preset standard, and the trend of the validation curve is close to the trend of the set demand covariance curve. Ensuring the integrity of power data and determining the important indicators of power lines are more conducive to the safe and stable operation of the power data center.

Hierarchical and Multi-Group Data Sharing for Cloud-Assisted Industrial Internet of Things

With the development of Industrial Internet of Things (IIoT) and 5G, massive data are easily collected and transmitted in cloud. Therefore, it is critical to guarantee the security of data sharing. In IIoT applications, the users of a group are in hierarchical structure and they intend to access data by external groups, which requires fine-grained access control, data authenticity and data retrieval. However, existing approaches rarely provide such solutions to satisfy these requirements simultaneously. In this paper, we propose an efficient hierarchical and multi-group data sharing framework (HMGDSF) in cloud-assisted IIoT. Apart from fine-grained data access control for hierarchical users with key leakage resilience, HMGDSF achieves user anonymity with traceability and keyword-based data retrieval. Moreover, the approach supports data authenticity and integrity verification for multi-group data sharing by integrating group signature mechanism. We provide proof for the security of framework and demonstrate its efficiency and practicability by extensive evaluations.

A Hybrid Multi-user Cloud Access Control based Block Chain Framework for Privacy Preserving Distributed Databases

Most of the traditional medical applications are insecure and difficult to compute the data integrity with variable hash size. Traditional medical data security systems are insecure and it depend on static parameters for data security. Also, distributed based cloud storage systems are independent of integrity computational and data security due to unstructured data and computational memory. As the size of the data and its dimensions are increasing in the public and private cloud servers, it is difficult to provide the machine learning based privacy preserving in cloud computing environment. Block-chain technology plays a vital role for large cloud databases. Most of the conventional block-chain frameworks are based on the existing integrity and confidentiality models. Also, these models are based on the data size and file format. In this model, a novel integrity verification and encryption framework is designed and implemented in cloud environment. In order to overcome these problems in the cloud computing environment, a hybrid integrity and security-based block-chain framework is designed and implemented on the large distributed databases. In this framework,a novel decision tree classifier is used along with non-linear mathematical hash algorithm and advanced attribute-based encryption models are used to improve the privacy of multiple users on the large cloud datasets. Experimental results proved that the proposed advanced privacy preserving based block-chain technology has better efficiency than the traditional block-chain based privacy preserving systems on large distributed databases.

An Improved Integrity-Based Hybrid Multi-User Data Access Control for Cloud Heterogeneous Supply Chain Databases

Cloud-based supply chain applications play a vital role in the multi-user data security framework for heterogeneous data types. The majority of the existing security models work effectively on small to medium-sized datasets with a homogenous data structure. In contrast, Supply Chain Management (SCM) systems in the real world utilize heterogeneous databases. The heterogeneous databases include a massive quantity of raw SCM data and a scanned image of a purchase quotation. In addition, as the size of the database grows, it becomes more challenging to provide data security on multi-user SCM databases. Multi-user datatypes are heterogeneous in structure, and it is complex to apply integrity and confidentiality models due to high computational time and resources. Traditional multi-user integrity algorithms are difficult to process heterogeneous datatypes due to computational time and variation in hash bit size. Conventional attribute-based encryption models such as "Key-policy attribute-based encryption" (KP-ABE), "Ciphertext-Policy Attribute-Based Encryption" (CP-ABE) etc., are used to provide strong data confidentiality on large textual data. Providing security for heterogeneous databases in a multi-user SCM system requires a significant computational runtime for these conventional models. An enhanced integrity-based multi-user access control security model is created for heterogeneous databases in the cloud infrastructure to address the problems with heterogeneous SCM databases. A non-linear integrity model is developed to provide strong integrity verification in the multi-user communication process. A multi-user based access control model is implemented by integrating the multi-user hash values in the encoding and decoding process. Practical results proved that the multi-user non-linear integrity-based multi-access control framework has better runtime and hash bit variation compared to the conventional models on large cloud-based SCM databases.

Blockchain assisted encryption scheme for intellectual share estimation using medical research data

SummaryRecently, blockchain utilization with advanced encryption and decryption has gained improved attention over diverse applications. Especially in the medical field, big data storage requires an effective medium for managing user data with high confidentiality. The existing research presents diverse encryption algorithms, but certain drawbacks include sensitive information leakage, ineffective data integrity verification, and increased computational cost and time. The proposed work develops an effective blockchain mechanism for secured data storage to conquer these limitations. This article secures the medical research data from different team members for intellectual share estimation. Initially, the registration process involves user details such as name, contact number, age and so on. Next, a digital signature is generated for every registered user using the SHA‐256 hash algorithm. During data uploading, the data is encrypted using elliptic curve Diffie Hellman encryption scheme to promote security. The audio and video files are stored in inter planetary file system, and texts are stored in the blockchain. The users can retrieve the stored data from the blockchain using the encryption key. The identities and associated public keys are verified using a smart contract to validate the user certificate. Next, a decision form is made to show the polling mechanism using item preference ranking for other users viewing the data. The proposed mechanism ensures intellectual shares for all the users associated with a specified project. The performances are analyzed using PYTHON, and the encryption time obtained in the proposed model is 0.85 s at 140 security bit level.

Clustering-based data integrity verification approach for multi-replica in a fog environment

Clustering-based data integrity verification approach for multi-replica in a fog environment

Backdoor-Resistant Public Data Integrity Verification Scheme Based on Smart Contracts

This paper analyzes existing smart contract-based public data integrity verification schemes and identifies certain weaknesses. First, the fair arbitration mechanism deployed in these schemes fails to meet the users’ requirements as it may not promptly notify users of data corruption or loss. Second, to ensure outsourced data confidentiality, existing data integrity schemes use a conventional encrypted method, where each user randomly selects a key to encrypt the outsourced data. Such a method results in varying ciphertexts for the same data by different users, leading to additional storage costs for the cloud server. Third, users’ devices, if poorly designed or even intentionally backdoored, can potentially exfiltrate secrets and compromise the security of schemes. To address these issues, we propose the first backdoor-resistant public data integrity verification scheme based on smart contracts (ASSIST). The key idea is to introduce a new entity (a whistleblower) to periodically monitor the state of verification results recorded in the blockchain. This allows for timely notification of data corruption to users. ASSIST requires users to encrypt their data with a cryptographic primitive called message-locked encryption (MLE), which motivates different users to produce the same ciphertext for the same data and reduces storage costs for cloud servers. We also deploy a cryptographic reverse firewall between users’ devices and the external to re-randomize interactive messages, making the exfiltration impossible. We provide rigorous security proofs to demonstrate the security of ASSIST. The performance evaluation shows that ASSIST is efficient regarding computation and communication costs.

A variable dimensional chaotic map-based medical image encryption algorithm with multi-mode.

Since the COVID-19 pandemic, telemedicine or non-face-to-face medicine has increased significantly. In practice, various types of medical images are essential to achieve effective telemedicine. Medical image encryption algorithms play an irreplaceable role in the fast and secure transmission and storage of these medical images. However, most of the existing medical image encryption algorithms are full encryption algorithms, which are inefficient and time-consuming, so they are not suitable for emergency medical scenarios. To improve the efficiency of encryption, a small number of works have focused on partial or selective encryption algorithms for medical images, in which different levels of encryption strategies were adopted for different information content regions of medical images. However, these encryption algorithms have inadequate security more or less. In this paper, based on the Logistic map, we designed an improved variable dimension map. Then, an encryption algorithm for medical images was proposed based on it. This algorithm has two modes: (1) full encryption mode and (2) semi-full encryption mode, which can better adapt to different medical scenarios, respectively. In full encryption mode, all pixels of medical images are encrypted by using the confusion-diffusion structure. In semi-full encryption mode, the region of interest of medical images is extracted. The confusion was first adopted to encrypt the region of interest, and then, the diffusion was adopted to encrypt the entire image. In addition, no matter which encryption mode is used, the algorithm provides the function of medical image integrity verification. The proposed algorithm was simulated and analyzed to evaluate its effectiveness. The results show that in semi-full encryption mode, the algorithm has good security performance and lower time consumption; while in full encryption mode, the algorithm has better security performance and is acceptable in time.

An Efficient and Universal Real-Time Data Integrity Verification Scheme Based on Symmetric Key in Stream Computing System

The integrity of real-time data streams has not been solved for a long time and has gradually become a difficult problem in the field of data security. Most of the current data integrity verification schemes are constructed using cryptographic algorithms with complex computation, which cannot be directly applied to real-time stream computing systems. Aiming at the above issue, this paper adopts the Carter–Wegman MAC method, pseudo-random function and symmetric cryptography mechanism to construct the Real-Time Data Integrity Verification scheme based on symmetric key in stream computing systems (RT-DIV), which converts a one-time MAC to a multiple-time MAC and retains the advantage of security performance. Then, a security analysis is given under the standard model. Finally, experiments and data analysis are conducted in a simulated environment, and the experimental results show that the RT-DIV scheme can effectively guarantee the integrity of real-time data streams. Furthermore, the RT-DIV scheme lays the foundation for the secure application of the stream computing system.

Efficient Integrity Auditing Mechanism With Secure Deduplication for Blockchain Storage

Massive nodes in a blockchain form an off-chain distributed storage network to provide storage resources for users to meet large data upload requirements. However, this storage approach introduces security and performance issues. Firstly, it is difficult to guarantee the integrity of the data uploaded, and these data may be easily corrupted or lost. Moreover, uploading excessive duplicate data leads to a waste of storage resources. In this study, to address these issues, with a double-copy storage model for blockchain off-chain storage, a novel public auditing scheme with client-side deduplication is proposed to reduce the storage overhead of nodes and check the integrity of the off-chain data. Based on smart contracts, our scheme could realize efficient user ownership and off-chain data integrity verification automatically. In addition, both data encryption and deduplication are achieved based on message-locked encryption and an improved authenticator generation algorithm. Security analysis and experimental comparisons show that the proposed scheme is effective and practical.

Research on Accounting Information Integrity Verification Method based on Blockchain

Conventional accounting information integrity verification method mainly uses SOA mode to obtain verification parameters, which is susceptible to the influence of cloud memory verification transmission bandwidth, resulting in high information integrity verification overhead, therefore, this paper designs a new accounting information integrity verification method based on blockchain. The experimental results show that the designed blockchain-based accounting information integrity verification method has a smaller verification overhead under different data blocks, which proves that the designed accounting information integrity verification method has a better verification effect, has the advantage of low energy consumption, has a certain application value, and makes certain methodological innovations for solving the distortion problem of accounting information.

Public Blockchain-Based Data Integrity Verification for Low-Power IoT Devices

The integration of sensor nodes with public blockchains is possible with the help of low-power communication networks that use Bluetooth Low Energy and LoRa. However, power-consuming Wi-Fi is still the main means of communication for the existing sensor nodes, especially in urban environments. Typically high power consumption, private key disclosure, and high transaction fees are the issues that prevent battery-powered sensor nodes from being integrated with a public blockchain. Therefore, this paper proposes a data protection protocol that is able to secure the data integrity of the stored sensor data, help to reduce transaction fees, and prolong battery life for IoT devices that are used with public blockchain networks. A proof of concept is presented using an ESP32S2 device to evaluate and verify the performance of the proposed data storage protocol. A smart contract is designed and analyzed using a formal smart contract analysis tool. A decentralized web application is designed to display and verify the sensor data extracted from the public blockchain. The power consumption, memory usage, and security of the proposed solution are evaluated. The evaluation results show that data integrity can be achieved even for low-power sensor nodes that connect to public blockchains via Wi-Fi network.

Forensic integrity verification of video recordings based on MTS files.

Digital video is used in criminal trials as evidence with legal responsibility because video content vividly depicts events occurring at a crime scene. However, using sophisticated video editing software, assailants can easily manipulate visible clues for their own benefit. Therefore, the integrity of digital video files acquired or submitted as evidence must be ensured. Forensic analysis of digital video is key to ensuring the integrity of links with individual cameras. In this study, we analyzed whether it is possible to ensure the integrity of MTS video files. Herein, we propose a method to verify the integrity of MTS files encoded by advanced video coding high definition (AVCHD), which is frequently used for video recording. To verify MTS file integrity, we propose five features. Codec information, picture timing, and camera manufacture/model are modified AVI and MP4-like format video verification features. Group of pictures and Universally Unique Identifier patterns were specifically developed for MTS streams. We analyzed the features of 44 standard files recorded using all recording options of seven cameras. We checked whether integrity can be validated on unmanipulated videos recorded in various environments. In addition, we considered whether manipulated MTS files edited in video editing software could be validated. Experimental results show that all unmanipulated and manipulated MTS files with known recording devices were discriminated only when all five features were checked. These results show that the proposed method verifies the integrity of MTS files, strengthening the validity of MTS file-based evidence in trials.