AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A detection framework based on noise-invariant contrastive learning to enhance adversarial robustness by aligning feature representations of original and noise-augmented samples, which significantly outperforms conventional adversarial training.
FDT-PC (Frequency Domain Transformation with Perceptual Constraints), a novel method that enhances adversarial transferability across different model architectures, is proposed, which achieves superior black-box attack performance on both CNNs and Vision Transformers, outperforming existing state-of-the-art input trans...
An ensemble-based approach, namely the X-Iv2 Ensemble approach, merging Inception ResNet v2 and Xception Net based on their complementary architectures to enhance feature extraction and classification is introduced, integrating Explainable AI (XAI) using Integrated Gradients to interpret decision-making processes.
This paper introduces a new anagram based attack into the literature and trains the model to learn these representations by adding a novel label to the dataset which is used as a feature for training the model.
This work explores an approach that integrates wavelet-based frequency analysis with deep learning to enhance deepfake detection, and suggests that wavelet sub-bands expose manipulation cues that are useful for detecting unseen fake classes, but they should not be interpreted as a uniform robustness improvement.
The research methodology involved a systematic literature review using the Scopus database, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and focusing on recent advancements in attack and defence techniques.
A robust, explainable detection framework is presented that combines a CNN backbone for extracting spatial artifacts with an LSTM module for modeling temporal inconsistencies across frames that enhances forensic decision support and increases practical readiness for content verification systems.
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