Perceptual Hashing Research Articles

Secure multimodal biometric authentication framework with perceptual hashing and lightweight cryptography

Secure multimodal biometric authentication framework with perceptual hashing and lightweight cryptography

Blockchain for video watermarking: An enhanced copyright protection approach for video forensics based on perceptual hash function.

As a direct result of advancements in digital technology and the Internet, the copyright protection and information integrity of multimedia that are being published across the Internet have emerged as a major and urgent issue that needs to be addressed. The technique of digital watermarking may be used to protect intellectual property. In terms of authentication, resilience, storage, and capacity of digital watermarking information, there is still room for development. Blockchain's potential in video copyright protection and management applications has motivated researchers. Copyright owners and consumers may now communicate directly via copyright protection apps built on the blockchain, eliminating the need for distributers and the associated fees. Nonetheless, the current blockchain-based video watermarking solutions require storing a significant number of coordinates depending on the watermark size and are susceptible to video frame attacks on the video frame texture region. This study proposes an enhanced video copyright management approach that incorporates digital watermarking, the blockchain, and a perceptual hash function. Watermark information is stored on a blockchain structure, which also acts as a timestamp for verification purposes. To verify watermark data without the source video, a perceptual hash function is employed to compute a hash value based on the structural information of video frames. The contribution is in learning how to extract a hash function from a small number of video frames that still adequately represent a large amount of video content while also reducing the number of unnecessary video frames and the amount of computation required to summarize and index that content in a blockchain. This expedites the dissemination of copyrighted works and increases their security and readability, hence facilitating their circulation on the Internet. Our experimental results demonstrate that this approach is memory efficient, as it only needs to store one key for each key frame, regardless of the size of the watermark. Additionally, the overall robustness is greatly improved by using the blockchain's random hash function. Therefore, new and important advancements in video watermarking have been realized because of this effort.

Distance distributions and runtime analysis of perceptual hashing algorithms

Perceptual image hashing refers to a class of algorithms that produce content-based image hashes. These systems use specialized perceptual hash algorithms like Phash, Microsoft’s PhotoDNA, or Facebook’s PDQ to generate a compact digest of an image file that can be roughly compared to a database of known illicit-content digests. Time taken by perceptual hashing algorithms to generate hash code has been computed. Then, we evaluated perceptual hashing algorithms on two million dataset of images. The produced nine variants of the original images were computed and then several distances were calculated. There have been several studies in the past, but in the existing literature size of the data is small and there are very few studies with hash code computation time and robustness tradeoff. This work shows that existing perceptual hashing algorithms are robust for most of the content-preserving operations and there is a tradeoff between computation time and robustness.

DRPChain: A new blockchain-based trusted DRM scheme for image content protection

The unauthorized replication and distribution of digital images pose significant challenges to copyright protection. While existing solutions incorporate blockchain-based techniques such as perceptual hashing and digital watermarking, they lack large-scale experimental validation and a dedicated blockchain consensus protocol for image copyright management. This paper introduces DRPChain, a novel digital image copyright management system that addresses these issues. DRPChain employs an efficient cropping-resistant robust image hashing algorithm to defend against 14 common image attacks, demonstrating an 85% success rate in watermark extraction, 10% higher than the original scheme. Moreover, the paper designs the K-Raft consensus algorithm tailored for image copyright protection. Comparative experiments with Raft and benchmarking against PoW and PBFT algorithms show that K-Raft reduces block error rates by 2%, improves efficiency by 300ms compared to Raft, and exhibits superior efficiency,decentralization, and throughput compared to PoW and PBFT. These advantages make K-Raft more suitable for digital image copyright protection. This research contributes valuable insights into using blockchain technology for digital copyright protection, providing a solid foundation for future exploration.

A fast and efficient large-scale near duplicate image retrieval system using double perceptual hashing

A fast and efficient large-scale near duplicate image retrieval system using double perceptual hashing

AI support for colonoscopy quality control using CNN and transformer architectures

BackgroundConstruct deep learning models for colonoscopy quality control using different architectures and explore their decision-making mechanisms.MethodsA total of 4,189 colonoscopy images were collected from two medical centers, covering different levels of bowel cleanliness, the presence of polyps, and the cecum. Using these data, eight pre-trained models based on CNN and Transformer architectures underwent transfer learning and fine-tuning. The models’ performance was evaluated using metrics such as AUC, Precision, and F1 score. Perceptual hash functions were employed to detect image changes, enabling real-time monitoring of colonoscopy withdrawal speed. Model interpretability was analyzed using techniques such as Grad-CAM and SHAP. Finally, the best-performing model was converted to ONNX format and deployed on device terminals.ResultsThe EfficientNetB2 model outperformed other architectures on the validation set, achieving an accuracy of 0.992. It surpassed models based on other CNN and Transformer architectures. The model’s precision, recall, and F1 score were 0.991, 0.989, and 0.990, respectively. On the test set, the EfficientNetB2 model achieved an average AUC of 0.996, with a precision of 0.948 and a recall of 0.952. Interpretability analysis showed the specific image regions the model used for decision-making. The model was converted to ONNX format and deployed on device terminals, achieving an average inference speed of over 60 frames per second.ConclusionsThe AI-assisted quality system, based on the EfficientNetB2 model, integrates four key quality control indicators for colonoscopy. This integration enables medical institutions to comprehensively manage and enhance these indicators using a single model, showcasing promising potential for clinical applications.

Onion-Hash: A Compact and Robust 3D Perceptual Hash for Asset Authentication

The digitalization of manufacturing processes and recent trends, such as the Industrial Metaverse, are continuously increasing in adoption in various critical industries, resulting in a surging demand for 3D CAD models and their exchange. Following this, it becomes necessary to protect the intellectual property of content designers in increasingly decentralized production environments where 3D assets are repeatedly shared online within the ecosystem. CAD models can be protected by traditional security methods such as watermarking, which embeds additional information into the file. Nevertheless, malicious actors may find ways to remove the information from a file. To authenticate and protect 3D models without relying on additional information, we propose a robust 3D perceptual hash generated based on the prevalent geometric features. Furthermore, our geometry-based approach generates compact and tamper-resistant fingerprints for a 3D model by projecting multiple spherical sliced layers of intersection points into cluster distances. The resulting hash links the 3D model to an owner, supporting the detection of counterfeits. The approach was benchmarked for similarity search and evaluated against established state-of-the-art shape retrieval techniques. The results show promising resistance against arbitrary transformations and manipulations, with our approach detecting 25.6% more malicious tampering attacks than the baseline.

Integrating Fresnelet Transform and Spiking Cortical Model for robust medical image cryptosystem in zero-watermarking

Medical imaging is essential in modern healthcare as it enables accurate diagnosis, planning of therapies, and monitoring of various medical problems. The security and integrity of medical images have become of utmost importance due to the growing dependence on digital imaging technologies. Zero-watermarking is a technique used in digital image processing where the watermark is not embedded into the medical image instead, a key is generated for security purposes to achieve the information process. In the context of medical images, preserving visual fidelity and diagnostic accuracy is of utmost importance, making zero-watermarking an attractive approach for safeguarding sensitive healthcare data. This paper proposes a zero-watermarking system that utilizes the Frensnelet Transform and Spiking Cortical Model (FT-SCM) for extracting image features and enhancing the system’s resilience against various attacks. The proposed system uses FT-SCM techniques to generate a perceptual hash of size 1 × 64. The proposed cryptosystem is used to encrypt the watermark for better security. Later, the encrypted watermark and perceptual hash are merged to create a zero-watermark image (key). The proposed system yields an average PSNR, SSIM, and NC values of 57.07 dB, 0.99, and 0.75, respectively, which demonstrates an improved performance from the state-of-the-art zero-watermarking approaches. Also, the proposed cryptosystem is analysed and compared using security metrics. Security assessments demonstrate the proposed image cryptosystem approach’s efficiency, security, and realizability because of key generation in the confusion and diffusion process, which gives a chaotic nature compared to state-of-the-art approaches encryption methods.

A generalized detection framework for covert timing channels based on perceptual hashing

AbstractNetwork covert channels use network resources to transmit data covertly, and their existence will seriously threaten network security. Therefore, an effective method is needed to prevent and detect them. Current network covert timing channel detection methods often incorporate machine learning methods in order to achieve generalized detection, but they consume a large amount of computational resources. In this paper, we propose a generalized detection framework for covert channels based on perceptual hashing without relying on machine learning methods. And we propose a one‐dimensional data feature descriptor for feature extraction of perceptual hash for the data characteristics of covert timing channels. We first generate the hash sequence of the corresponding channel to get the average hash, which is used for comparison in the test phase. The experimental results show that the feature descriptor can capture the feature differences of one‐dimensional data well. When compared to machine learning methods, this perceptual hashing algorithms enable faster traffic detection. Meanwhile, our method is able to detect the effectiveness with the smallest coverage window compared with the latest solutions. Moreover, it exhibits robustness in jitter network environment.

PFDup: Practical Fuzzy Deduplication for Encrypted Multimedia Data

Redundant data wastes cloud storage space, especially the multimedia data which comprises a large number of similar files and accounts for the majority of cloud storage. To protect privacy and eliminate redundancy in the cloud, fuzzy deduplication for encrypted multimedia data is practical and feasible. Unfortunately, existing fuzzy deduplications depend on aided server to be against security threats. In this paper, we propose a Practical Fuzzy Deduplication (PFDup) algorithm for encrypted multimedia data and it is secure against brute-force guessing attacks without additional independent severs. With our secure fuzzy deduplication technology, cloud storage can be significantly optimized by using Perceptual Hash (phash) to eliminate large quantities of identical even the similar multimedia data in a secure manner. In addition, PFDup protocol supports label consistency and a non-interactive Proof of Ownership (PO) in order to prevent the server–client collusion attacks. We conduct a series of experiments on numerous real-world datasets and the simulation results show that our deduplication rate for the similar images is over 91.5%.

Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images.

There is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions. The aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns. We design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images. Compared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules. The proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations.

PHASER: Perceptual hashing algorithms evaluation and results - An open source forensic framework

The automated comparison of visual content is a contemporary solution to scale the detection of illegal media and extremist material, both for detection on individual devices and in the cloud. However, the problem is difficult, and perceptual similarity algorithms often have weaknesses and anomalous edge cases that may not be clearly documented. Additionally, it is a complex task to perform an evaluation of such tools in order to best utilise them. To address this, we present PHASER, a still-image perceptual hashing framework enabling forensics specialists and scientists to conduct experiments on bespoke datasets for their individual deployment scenarios. The framework utilises a modular approach, allowing users to specify and define a perceptual hash/image transform/distance metric triplet, which can be explored to better understand their behaviour and interactions. PHASER is open-source and we demonstrate its utility via case studies which briefly explore setting an appropriate dataset size and the potential to optimise the performance of existing algorithms by utilising learned weight vectors for comparing hashes.

Auto-authentication watermarking scheme based on CNN and perceptual hash function in the wavelet domain

Auto-authentication watermarking scheme based on CNN and perceptual hash function in the wavelet domain

Perceptual Hashing on Image-based Malware Detection

Information delivery through communications in the digital age is aided by visuals. Malicious actors also can utilize images to convey malevolent communication. This study attempts to assess the viability of detecting image-based malware by utilizing perceptual hashes: average hashing, difference hashing, perceptive hashing, and wavelet hashing. The proposed method allows the determination of the aforementioned perceptual hashes’ effectiveness against malware embedded in the images. The approach used to achieve this study objective is methodological experiments through comparisons of 26 images and 6 malware variants against selected perceptual hashing. The proposed method involves adding malware in pictures before and after the EOF bytes. Our research shows that perceptual hashing methods are vulnerable to malware in images, urging the development of new, more effective methods for malware detection.

Perceptual Hashing With Deep and Texture Features

Perceptual Hashing With Deep and Texture Features

ChaSAM: An Architecture Based on Perceptual Hashing for Image Detection in Computer Forensics

ChaSAM: An Architecture Based on Perceptual Hashing for Image Detection in Computer Forensics

Accurate facial expression recognition method based on perceptual hash algorithm

Accurate facial expression recognition method based on perceptual hash algorithm

Accurate facial expression recognition method based on perceptual hash algorithm

Accurate facial expression recognition method based on perceptual hash algorithm

XAI Driven Intelligent IoMT Secure Data Management Framework.

The Internet of Medical Things (IoMT) has transformed traditional healthcare systems by enabling real-time monitoring, remote diagnostics, and data-driven treatment. However, security and privacy remain significant concerns for IoMT adoption due to the sensitive nature of medical data. Therefore, we propose an integrated framework leveraging blockchain and explainable artificial intelligence (XAI) to enable secure, intelligent, and transparent management of IoMT data. First, the traceability and tamper-proof of blockchain are used to realize the secure transaction of IoMT data, transforming the secure transaction of IoMT data into a two-stage Stackelberg game. The dual-chain architecture is used to ensure the security and privacy protection of the transaction. The main-chain manages regular IoMT data transactions, while the side-chain deals with data trading activities aimed at resale. Simultaneously, the perceptual hash technology is used to realize data rights confirmation, which maximally protects the rights and interests of each participant in the transaction. Subsequently, medical time-series data is modeled using bidirectional simple recurrent units to detect anomalies and cyberthreats accurately while overcoming vanishing gradients. Lastly, an adversarial sample generation method based on local interpretable model-agnostic explanations is provided to evaluate, secure, and improve the anomaly detection model, as well as to make it more explainable and resilient to possible adversarial attacks. Simulation results are provided to illustrate the high performance of the integrated secure data management framework leveraging blockchain and XAI, compared with the benchmarks.

Large-scale image dataset for perceptual hashing

Large-scale image dataset for perceptual hashing