Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment

[EN] Due to large volume and high variability of editing tools, protecting multimedia contents, and ensuring their privacy and authenticity has become an increasingly important issue in cyber-physical security of industrial environments, especially industrial surveillance. The approaches authenticating images using their principle content emerge as popular authentication techniques in industrial video surveillance applications. But maintaining a good tradeoff between perceptual robustness and discriminations is the key research challenge in image hashing approaches. In this paper, a robust image hashing method is proposed for efficient authentication of keyframes extracted from surveillance video data. A novel feature extraction strategy is employed in the proposed image hashing approach for authentication by extracting two important features: the positions of rich and nonzero low edge blocks and the dominant discrete cosine transform (DCT) coefficients of the corresponding rich edge blocks, keeping the computational cost at minimum. Extensive experiments conducted from different perspectives suggest that the proposed approach provides a trustworthy and secure way of multimedia data transmission over surveillance networks. Further, the results vindicate the suitability of our proposal for real-time authentication and embedded security in smart industrial applications compared to state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province, China.Sajjad, M.; Ul Haq, I.; Lloret, J.; Ding, W.; Muhammad, K. (2019). Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment. IEEE Transactions on Industrial Informatics. 15(12):6541-6550. https://doi.org/10.1109/TII.2019.2921652S65416550151

Adaptive CSLBP compressed image hashing

Hashing is popular technique of image authentication to identify malicious attacks and it also allows appearance changes in an image in controlled way. Image hashing is quality summarization of images. Quality summarization implies extraction and representation of powerful low level features in compact form. Proposed adaptive CSLBP compressed hashing method uses modified CSLBP (Center Symmetric Local Binary Pattern) as a basic method for texture extraction and color weight factor derived from L*a*b* color space. Image hash is generated from image texture. Color weight factors are used adaptively in average and difference forms to enhance discrimination capability of hash. For smooth region, averaging of colours used while for non-smooth region, color differencing is used. Adaptive CSLBP histogram is a compressed form of CSLBP and its quality is improved by adaptive color weight factor. Experimental results are demonstrated with two benchmarks, normalized hamming distance and ROC characteristics. Proposed method successfully differentiate between content change and content persevering modifications for color images

Modified CSLBP

Image hashing is an efficient way to handle digital data authentication problem. Image hashing represents quality summarization of image features in compact manner. In this paper, the modified center symmetric local binary pattern (CSLBP) image hashing algorithm is proposed. Unlike CSLBP 16 bin histogram, Modified CSLBP generates 8 bin histogram without compromise on quality to generate compact hash. It has been found that, uniform quantization on a histogram with more bin results in more precision loss. To overcome quantization loss, modified CSLBP generates the two histogram of a four bin. Uniform quantization on a 4 bin histogram results in less precision loss than a 16 bin histogram. The first generated histogram represents the nearest neighbours and second one is for the diagonal neighbours. To enhance quality in terms of discrimination power, different weight factor are used during histogram generation. For the nearest and the diagonal neighbours, two local weight factors are used. One is the Standard Deviation (SD) and other is the Laplacian of Gaussian (LoG). Standard deviation represents a spread of data which captures local variation from mean. LoG is a second order derivative edge detection operator which detects edges well in presence of noise. The proposed algorithm is resilient to the various kinds of attacks. The proposed method is tested on database having malicious and non-malicious images using benchmark like NHD and ROC which confirms theoretical analysis. The experimental results shows good performance of the proposed method for various attacks despite the short hash length

Enhancement of the Fynbos Leaf Optical Recognition Application (FLORA-E)

Object perception, classification and similarity discernment are relatively effortless tasks in humans. The exact method by which the brain achieves these is not yet fully understood. Identification, classification and similarity inference are currently nontrivial tasks for machine learning enabled platforms, even more so for ones operating in real time applications. This dissertation conducted research on the use of machine learning algorithms in object identification and classification by designing and developing an artificially intelligent Fynbos Leaf Optical Recognition Application (FLORA) platform. Previous versions of FLORA (versions A through D) were designed to recognise Proteaceae fynbos leaves by extracting six digital morphological features, then using the k-nearest neighbour (k-NN) algorithm for classification, yielding an 86.6% accuracy. The methods utilised in FLORA-A to -D are ineffective when attempting to classify irregular structured objects with high variability, such as stems and leafy stems. A redesign of the classification algorithms in the latest version, FLORA-E, was therefore necessary to cater for irregular fynbos stems. Numerous algorithms and techniques are available that can be used to achieve this objective. Keypoint matching, moments analysis and image hashing are the three techniques which were investigated in this thesis for suitability in achieving fynbos stem and leaf classification. These techniques form active areas of research within the field of image processing and were chosen because of their affine transformation invariance and low computational complexity, making them suitable for real time classification applications. The resulting classification solution, designed from experimentation on the three techniques under investigation, is a keypoint matching – Hu moment hybrid algorithm who`s output is a similarity index (SI) score that is used to return a ranked list of potential matches. The algorithm showed a relatively high degree of match accuracy when run on both regular (leaves) and irregular (stems) objects. The algorithm successfully achieved a top 5 match rate of 76% for stems, 86% for leaves and 81% overall when tested using a database of 24 fynbos species (predominantly from the Proteaceae family), where each species had approximately 50 sample images. Experimental results show that Hu moment and keypoint classifiers are ideal for real time applications because of their fast-matching capabilities. This allowed the resulting hybrid algorithm to achieve a nominal computation time of ~0.78s per sample on the test apparatus setup for this thesis. The scientific objective of this thesis was to build an artificially intelligent platform capable of correctly classifying fynbos flora by conducting research on object identification and classification algorithms. However, the core driving factor is rooted in the need to promote conservation in the Cape Floristic Region (CFR). The FLORA project is an example of how science and technology can be used as effective tools in aiding conservation and environmental awareness efforts. The FLORA platform can also be a useful tool for professional botanists, conservationists and fynbos enthusiasts by giving them access to an indexed and readily available digital catalogue of fynbos species across the CFR

Implementação fotónica de funções fisicamente não clonáveis

This dissertation aimed to study and develop optical Physically Unclonable Functions, which are physical devices characterized by having random intrinsic variations, thus being eligible towards high security systems due to their unclonability, uniqueness and randomness. With the rapid expansion of technologies such as Internet of Things and the concerns around counterfeited goods, secure and resilient cryptographic systems are in high demand. Moreover the development of digital ecosystems, mobile applications towards transactions now require fast and reliable algorithms to generate secure cryptographic keys. The statistical nature of speckle-based imaging creates an opportunity for these cryptographic key generators to arise. In the scope of this work, three different tokens were implemented as physically unclonable devices: tracing paper, plastic optical fiber and an organic-inorganic hybrid. These objects were subjected to a visible light laser stimulus and produced a speckle pattern which was then used to retrieve the cryptographic key associated to each of the materials. The methodology deployed in this work features the use of a Discrete Cosine Transform to enable a low-cost and semi-compact 128-bit key encryption channel. Furthermore, the authentication protocol required the analysis of multiple responses from different samples, establishing an optimal decision threshold level that maximized the robustness and minimized the fallibility of the system. The attained 128-bit encryption system performed, across all the samples, bellow the error probability detection limit of 10-12, showing its potential as a cryptographic key generator.Nesta dissertação pretende-se estudar e desenvolver Funções Fisicamente Não Clonáveis, dispositivos caracterizados por terem variações aleatórias intrínsecas, sendo, portanto, elegíveis para sistemas de alta segurança devido à sua impossibilidade de clonagem, unicidade e aleatoriedade. Com a rápida expansão de tecnologias como a Internet das Coisas e as preocupações com produtos falsificados, os sistemas criptográficos seguros e resilientes são altamente requisitados. Além disso, o desenvolvimento de ecossistemas digitais e de aplicações móveis para transações comerciais requerem algoritmos rápidos e seguros de geração de chaves criptográficas. A natureza estatística das imagens baseadas no speckle cria uma oportunidade para o aparecimento desses geradores de chaves criptográficas. No contexto deste trabalho, três dispositivos diferentes foram implementados como funções fisicamente não clonáveis, nomeadamente, papel vegetal, fibra ótica de plástico e um híbrido orgânico-inorgânico. Estes objetos foram submetidos a um estímulo de luz coerente na região espectral visível e produziram um padrão de speckle o qual foi utilizado para recuperar a chave criptográfica associada a cada um dos materiais. A metodologia implementada neste trabalho incorpora a Transformada Discreta de Cosseno, o que possibilita a criação de um sistema criptográfico de 128 bits caracterizado por ser semi-compacto e de baixo custo. O protocolo de autenticação exigiu a análise de múltiplas respostas de diferentes Physically Unclonable Functions (PUFs), o que permitiu estabelecer um nível de limite de decisão ótimo de forma a maximizar a robustez e minimizar a probabilidade de erro por parte do sistema. O sistema de encriptação de 128 bits atingiu valores de probabilidade de erro abaixo do limite de deteção, 10-12, para todas as amostras, mostrando o seu potencial como gerador de chaves criptográficas.Mestrado em Engenharia Físic

A deep locality-sensitive hashing approach for achieving optimal ‎image retrieval satisfaction

Efficient methods that enable high and rapid image retrieval are continuously needed, especially with the large mass of images that are generated from different sectors and domains like business, communication media, and entertainment. Recently, deep neural networks are extensively proved higher-performing models compared to other traditional models. Besides, combining hashing methods with a deep learning architecture improves the image retrieval time and accuracy. In this paper, we propose a novel image retrieval method that employs locality-sensitive hashing with convolutional neural networks (CNN) to extract different types of features from different model layers. The aim of this hybrid framework is focusing on both the high-level information that provides semantic content and the low-level information that provides visual content of the images. Hash tables are constructed from the extracted features and trained to achieve fast image retrieval. To verify the effectiveness of the proposed framework, a variety of experiments and computational performance analysis are carried out on the CIFRA-10 and NUS-WIDE datasets. The experimental results show that the proposed method surpasses most existing hash-based image retrieval methods

Functional mobile-based two-factor authentication by photonic physical unclonable functions

Given the rapid expansion of the Internet of Things and because of the concerns around counterfeited goods, secure and resilient cryptographic systems are in high demand. Due to the development of digital ecosystems, mobile applications for transactions require fast and reliable methods to generate secure cryptographic keys, such as Physical Unclonable Functions (PUFs). We demonstrate a compact and reliable photonic PUF device able to be applied in mobile-based authentication. A miniaturized, energy-efficient, and low-cost token was forged of flexible luminescent organic–inorganic hybrid materials doped with lanthanides, displaying unique challenge–response pairs (CRPs) for two-factor authentication. Under laser irradiation in the red spectral region, a speckle pattern is attained and accessed through conventional charge-coupled cameras, and under ultraviolet light-emitting diodes, it displays a luminescent pattern accessed through hyperspectral imaging and converted to a random intensity-based pattern, ensuring the two-factor authentication. This methodology features the use of a discrete cosine transform to enable a low-cost and semi-compact encryption system suited for speckle and luminescence-based CRPs. The PUF evaluation and the authentication protocol required the analysis of multiple CRPs from different tokens, establishing an optimal cryptographic key size (128 bits) and an optimal decision threshold level that minimizes the error probability.publishe