Research On Image Adversarial Algorithm Based On Deep Learnin

In recent years,deep learning has made significant breakthroughs in many areas,such as image classification and speech recognition,becoming a popular technology in the field of artificial intelligence.However,in practical applications,deep learning models often face the problem of adversarial attacks,which means that a small modification to the model’s input can cause the model’s output to make misclassifications or produce incorrect results.To solve this problem,research on adversarial examples is gradually getting attention.Adversarial examples refer to input data that can deceive deep learning models by making tiny perturbations to the original data.Adversarial examples pose a realistic threat to deep learning-based applications and systems and introduce security risks.Therefore,studying the causes,generation methods,defense strategies,etc.of adversarial examples is critical to improve the robustness and reliability of deep learning models.Research on adversarial examples can also help understand the decision process of deep learning models and promote the application of deep learning technology in more extensive fields.In this paper,we design adversarial defense and adversarial attack algorithms based on the deep learning model of image classification to promote security research in deep learning.The main research contents are summarized as follows:First,an adversarial example defense algorithm based on feature decoupling is proposed.The algorithm introduces the concepts of clean features and noisy features,and separates the two features from the input adversarial examples by feature decoupling-interaction;removes the adversarial perturbations by multiple cross-cycles,and reconstructs the image with clean features to improve the visual quality and classification accuracy.At the same time,it is proposed to use the features of the original clean image as prior knowledge to guide the network to learn the clean features of the adversarial examples.In addition,a classification loss function based on the CW attack is used to improve the adversarial robustness of the model.Experimental results show that the proposed algorithm achieves better defense performance under both standard tests and various attack tests,exceeding the standard test accuracy of the target classifier.Second,a novel adversarial example generation algorithm is proposed based on the combination of Transformer and GAN.The algorithm utilizes Transformer as a reconstruction network for receiving clean images and generating adversarial noise;and combines the discriminator based on deep convolutional networks to form a GAN network architecture to improve the authenticity of generated images and the stability of training.Meanwhile,an improved attention mechanism,Targeted Self-Attention,is proposed to guide the network model to learn to generate adversarial perturbations with specific attack targets by introducing target labels.Then,the adversarial noise is added to the clean examples using skip connections to form adversarial examples.Experimental results demonstrate that the proposed algorithm generates adversarial perturbations with small magnitudes,forming adversarial examples that satisfy the requirements of being indistinguishable by human vision.Compared with other generative-based methods,the proposed algorithm has higher generation efficiency and attack success rate and stronger applicability and scalability.