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Deena Babu Mandru

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Enhancing deep neural network robustness against projected gradient descent attacks through adversarial training

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By M. Kumar, Kiran Ramaswamy, E. Ramya Sree, Deena Babu Mandru
| Nature Verified
Abstract Deep neural networks (DNNs) remain vulnerable to adversarial examples, particularly to iterative white-box attacks such as Projected Gradient Descent (PGD). This study evaluates PGD adversarial training using a ResNet-18-inspired architecture adapted for single-channel 28 × 28 inputs. The architectural configuration replaces the conventional max-pooling stage with an identity mapping to preserve spatial information and retains residual connections throughout the feature extractor.
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