Instance-level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space

Yuwei Sun, Hideya Ochiai, Jun Sakuma

Abstract: Malicious perturbations embedded in input data, known as Trojan attacks, can cause neural networks to misbehave. However, the impact of a Trojan attack is reduced during fine-tuning of the model, which involves transferring knowledge from a pretrained large-scale model like visual question answering (VQA) to the target model. To mitigate the effects of a Trojan attack, replacing and fine-tuning multiple layers of the pretrained model is possible. This research focuses on sample efficiency, stealthiness and variation, and robustness to model fine-tuning. To address these challenges, we propose an instance-level Trojan attack that generates diverse Trojans across input samples and modalities. Adversarial learning establishes a correlation between a specified perturbation layer and the misbehavior of the fine-tuned model. We conducted extensive experiments on the VQA-v2 dataset using a range of metrics. The results show that our proposed method can effectively adapt to a fine-tuned model with minimal samples. Specifically, we found that a model with a single fine-tuning layer can be compromised using a single shot of adversarial samples, while a model with more fine-tuning layers can be compromised using only a few shots.