首页|New Findings from Koneru Lakshmaiah Education Foundation in the Area of Machine Learning Published (Momentum Iterative Fast Gradient Sign Algorithm for Adversarial Attacks and Defenses)
New Findings from Koneru Lakshmaiah Education Foundation in the Area of Machine Learning Published (Momentum Iterative Fast Gradient Sign Algorithm for Adversarial Attacks and Defenses)
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Data detailed on artificial intelligence have been presented. According to news originating from Vaddeswaram, India, by NewsRx correspondents, research stated, “Deep neural networks (DNNs) are particularly vulnerable to adversarial samples when used as machine learning (ML) models.” The news correspondents obtained a quote from the research from Koneru Lakshmaiah Education Foundation: “These kinds of samples are typically created by combining real-world samples with low-level sounds so they can mimic and deceive the target models. Since adversarial samples may switch between many models, black-box type attacks can be used in a variety of real-world scenarios. The main goal of this project is to produce an adversarial assault (white box) using PyTorch and then offer a defense strategy as a countermeasure. We developed a powerful offensive strategy known as the MI-FGSM (Momentum Iterative Fast Gradient Sign Method). It can perform better than the I-FGSM because to its adaptation (Iterative Fast Gradient Sign Method). The usage of MI-FGSM will greatly enhance transferability.”
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