Image Authentication Using Self-Supervised Learning to Detect Manipulation Over Social Network Platforms

Alkhowaiter, Mohammed; Almubarak, Khalid; Alyami, Mnassar; Alghamdi, Abdulmajeed; Zou, Cliff C.

doi:10.1109/milcom55135.2022.10017725

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Subsequently, several scholars have expanded upon the algorithm for further research [12][13][14] . Niu Xiamu [15] and colleagues clarified that Perceptual Hashing (PHashing) is a class of unidirectional mapping from multimedia datasets to perceptual datasets; Yin Yumei [16] and colleagues introduced the working principle of Perceptual Hash (PHash), Difference Hash(DHash) in image retrieval; Huang Jiaheng [16] and colleagues performed in animal images to introduce the working principle of Average Hash (AHash) ,and compared the efficiency of AHash, PHash and DHash [17][18] . In the existing map retrieval [19] , there are two main ways to retrieve metadata text information and retrieve map content [20][21] , in which the map content retrieval information is richer, and can analyze the spatial information and attribute information in the We-map data, and can also spatially analyze the We-map data to a certain extent, so as to obtain the We-map spatial relationship.…”

Section: Introductionmentioning

confidence: 99%

A method for retrieving We-map using perceptual hashing algorithms

Hou,

Yang,

Yan

et al. 2024

International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024)

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Section: Introductionmentioning

confidence: 99%

A method for retrieving We-map using perceptual hashing algorithms

Hou,

Yang,

Yan

et al. 2024

International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024)

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“…Deep neural network (DNN) theory, also called deep learning, accelerates the development of computer vision applications to advance the work presented in [2][3][4][5][6]. Unlike other ML approaches, DNNs can quickly learn complex patterns and representations from large and high-dimensional datasets.…”

Section: Introductionmentioning

confidence: 99%

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Alkhowaiter

Kholidy

Alyami

et al. 2023

Sensors

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Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that they all specifically target and exploit the neural networking structures in their designs. This understanding led us to develop a hypothesis that most classical machine learning models, such as random forest (RF), are immune to adversarial attack models because they do not rely on neural network design at all. Our experimental study of classical machine learning models against popular adversarial attacks supports this hypothesis. Based on this hypothesis, we propose a new adversarial-aware deep learning system by using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. Although the secondary classical machine learning model has less accurate output, it is only used for verification purposes, which does not impact the output accuracy of the primary deep learning model, and, at the same time, can effectively detect an adversarial attack when a clear mismatch occurs. Our experiments based on the CIFAR-100 dataset show that our proposed approach outperforms current state-of-the-art adversarial defense systems.

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