Younghoon Ban, Myeonghyun Kim, Haehyun Cho^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3535-3563, 2024, DOI:10.32604/cmes.2023.046658

Abstract Antivirus vendors and the research community employ Machine Learning (ML) or Deep Learning (DL)-based static analysis techniques for efficient identification of new threats, given the continual emergence of novel malware variants. On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus systems, in particular, have been identified as vulnerable to such AEs. This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers. Our attack method utilizes seven different perturbations, including Overlay Append, Section Append, and Break Checksum, capitalizing on the ambiguities present… More >

Chengsheng Yuan^1,2, Baojie Cui^1,2, Zhili Zhou³, Xinting Li^4,*, Qingming Jonathan Wu⁵

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 899-914, 2024, DOI:10.32604/cmc.2023.045854

Abstract In recent years, deep learning has been the mainstream technology for fingerprint liveness detection (FLD) tasks because of its remarkable performance. However, recent studies have shown that these deep fake fingerprint detection (DFFD) models are not resistant to attacks by adversarial examples, which are generated by the introduction of subtle perturbations in the fingerprint image, allowing the model to make fake judgments. Most of the existing adversarial example generation methods are based on gradient optimization, which is easy to fall into local optimal, resulting in poor transferability of adversarial attacks. In addition, the perturbation added to the blank area of… More >

Weizheng Wang^1,3, Xiangqi Wang^2,*, Xianmin Pan¹, Xingxing Gong³, Jian Liang³, Pradip Kumar Sharma⁴, Osama Alfarraj⁵, Wael Said⁶

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3859-3876, 2023, DOI:10.32604/cmc.2023.041346

Abstract Image-denoising techniques are widely used to defend against Adversarial Examples (AEs). However, denoising alone cannot completely eliminate adversarial perturbations. The remaining perturbations tend to amplify as they propagate through deeper layers of the network, leading to misclassifications. Moreover, image denoising compromises the classification accuracy of original examples. To address these challenges in AE defense through image denoising, this paper proposes a novel AE detection technique. The proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network (CNN) network structures. The used detector model integrates the classification results of different models as the input to the detector and calculates the… More >

Zhicong Qiu^1,2, Xianmin Wang^1,*, Huawei Ma¹, Songcao Hou¹, Jing Li^1,2,*, Zuoyong Li²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1243-1256, 2023, DOI:10.32604/iasc.2023.038241

Abstract Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where the latter is closer to… More >

Mingyu Dong, Diqun Yan^*, Rangding Wang

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1-12, 2023, DOI:10.32604/csse.2023.034568

Abstract Speech is easily leaked imperceptibly. When people use their phones, the personal voice assistant is constantly listening and waiting to be activated. Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices. To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech. Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail. Considering that the vulnerability of DNN can be used to protect the privacy in speech. In this work, we… More >

Kazim Ali^1,*, Adnan N. Qureshi¹, Muhammad Shahid Bhatti², Abid Sohail², Mohammad Hijji³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2237-2256, 2023, DOI:10.32604/iasc.2023.028810

Abstract Deep learning-based systems have succeeded in many computer vision tasks. However, it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks. These attacks can quickly spoil deep learning models, e.g., different convolutional neural networks (CNNs), used in various computer vision tasks from image classification to object detection. The adversarial examples are carefully designed by injecting a slight perturbation into the clean images. The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense, Generative Adversarial Network Defense, Deep Regret Analytic Generative Adversarial Networks Defense, Deep Denoising… More >

Kai Chen^*, Jinwei Wang, Jiawei Zhang

Journal of Information Hiding and Privacy Protection, Vol.4, No.1, pp. 15-24, 2022, DOI:10.32604/jihpp.2022.029006

Abstract In recent years, machine learning has become more and more popular, especially the continuous development of deep learning technology, which has brought great revolutions to many fields. In tasks such as image classification, natural language processing, information hiding, multimedia synthesis, and so on, the performance of deep learning has far exceeded the traditional algorithms. However, researchers found that although deep learning can train an accurate model through a large amount of data to complete various tasks, the model is vulnerable to the example which is modified artificially. This technology is called adversarial attacks, while the examples are called adversarial examples.… More >

Kazim Ali^1,*, Adnan N. Qureshi¹, Ahmad Alauddin Bin Arifin², Muhammad Shahid Bhatti³, Abid Sohail³, Rohail Hassan⁴

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2209-2224, 2022, DOI:10.32604/cmc.2022.020111

Abstract These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely affects the performance or prediction.… More >

Kazim Ali^*, Adnan N. Quershi

Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 271-284, 2022, DOI:10.32604/iasc.2022.021296

Abstract The current development of artificial intelligence is largely based on deep Neural Networks (DNNs). Especially in the computer vision field, DNNs now occur in everything from autonomous vehicles to safety control systems. Convolutional Neural Network (CNN) is based on DNNs mostly used in different computer vision applications, especially for image classification and object detection. The CNN model takes the photos as input and, after training, assigns it a suitable class after setting traceable parameters like weights and biases. CNN is derived from Human Brain's Part Visual Cortex and sometimes performs even better than Haman visual system. However, recent research shows… More >

Suliman A. Alsuhibany^*, Hessah Abdulaziz Alhodathi

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 523-537, 2022, DOI:10.32604/iasc.2022.019913

Abstract The Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) technique has been an interesting topic for several years. An Arabic CAPTCHA has recently been proposed to serve Arab users. Since there have been few scientific studies supporting a systematic design or tuning for users, this paper aims to analyze the Arabic text-based CAPTCHA at the parameter level by conducting an experimental study. Based on the results of this study, we propose an Arabic text-based CAPTCHA scheme with Fast Gradient Sign Method (FGSM) adversarial images. To evaluate the security of the proposed scheme, we ran four filter… More >