With the continuous growth in the number of parameters of transformer-based pretrained
language models (PLMs), particularly the emergence of large language models (LLMs) with …

In the field of natural language processing, the prevalent approach involves fine-tuning
pretrained language models (PLMs) using local samples. Recent research has exposed the …

Backdoor attacks have emerged as a prominent threat to natural language processing (NLP)
models, where the presence of specific triggers in the input can lead poisoned models to …

Textual backdoor attacks pose a practical threat to existing systems, as they can
compromise the model by inserting imperceptible triggers into inputs and manipulating …

Deep neural network (DNN) classifiers are potent instruments that can be used in various
security-sensitive applications. Nonetheless, they are vulnerable to certain attacks that …

Abstract We present a Multimodal Backdoor defense technique TIJO (Trigger Inversion
using Joint Optimization). Recently Walmer et al. demonstrated successful backdoor attacks …

Backdoor attacks have become a major security threat for deploying machine learning
models in security-critical applications. Existing research endeavors have proposed many …

Backdoor attacks inject poisoned samples into the training data, resulting in the
misclassification of the poisoned input during a model's deployment. Defending against …

Deep Neural Networks (DNNs) have led to unprecedented progress in various natural
language processing (NLP) tasks. Owing to limited data and computation resources, using …

Backdoor attacks have become an emerging threat to NLP systems. By providing poisoned
training data, the adversary can embed a" backdoor" into the victim model, which allows …