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Found 6 papers, 2 papers with code
Backdoor Attacks on Pre-trained Models by Layerwise Weight Poisoning
\textbf{P}re-\textbf{T}rained \textbf{M}odel\textbf{s} have been widely applied and recently proved vulnerable under backdoor attacks: the released pre-trained weights can be maliciously poisoned with certain triggers.
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.
Certified Robustness to Text Adversarial Attacks by Randomized [MASK]
Recently, few certified defense methods have been developed to provably guarantee the robustness of a text classifier to adversarial synonym substitutions.
SparseGAN: Sparse Generative Adversarial Network for Text Generation
It is still a challenging task to learn a neural text generation model under the framework of generative adversarial networks (GANs) since the entire training process is not differentiable.
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples
Despite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples.
Learning Structured Embeddings of Knowledge Graphs with Adversarial Learning Framework
A generative network (GN) takes two elements of a (subject, predicate, object) triple as input and generates the vector representation of the missing element.
