<ul><li>Researchers have developed the Enhanced Targeted DeepFool (ET DeepFool) algorithm for tailoring adversarial attacks on deep neural networks.</li><li>The algorithm allows for the specification of desired misclassification targets and incorporates a configurable minimum confidence score.</li><li>Preliminary outcomes suggest that certain models, including AlexNet and the Vision Transformer, exhibit robustness to the manipulations enabled by ET DeepFool.</li><li>The code for the algorithm is available on GitHub at https://github.com/FazleLabib/et_deepfool.</li></ul>

Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm

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