Pre-trained Vision-Language Models Learn Discoverable Visual Concepts

Do vision-language models (VLMs) pre-trained to caption an image of a "durian" learn visual concepts such as "brown" (color) and "spiky" (texture) at the same time? We aim to answer this question as visual concepts learned "for free" would enable wide applications such as neuro-symbolic reasoning or human-interpretable object classification. We assume that the visual concepts, if captured by pre-trained VLMs, can be extracted by their vision-language interface with text-based concept prompts. We observe that recent works prompting VLMs with concepts often differ in their strategies to define and evaluate the visual concepts, leading to conflicting conclusions. We propose a new concept definition strategy based on two observations: First, certain concept prompts include shortcuts that recognize correct concepts for wrong reasons; Second, multimodal information (e.g. visual discriminativeness, and textual knowledge) should be leveraged when selecting the concepts. Our proposed concept discovery and learning (CDL) framework is thus designed to identify a diverse list of generic visual concepts (e.g. "spiky" as opposed to "spiky durian"), which are ranked and selected based on visual and language mutual information. We carefully design quantitative and human evaluations of the discovered concepts on six diverse visual recognition datasets, which confirm that pre-trained VLMs do learn visual concepts that provide accurate and thorough descriptions for the recognized objects. All code and models are publicly released.

Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations

Adversarial attacking aims to fool deep neural networks with adversarial examples. In the field of natural language processing, various textual adversarial attack models have been proposed, varying in the accessibility to the victim model. Among them, the attack models that only require the output of the victim model are more fit for real-world situations of adversarial attacking. However, to achieve high attack performance, these models usually need to query the victim model too many times, which is neither efficient nor viable in practice. To tackle this problem, we propose a reinforcement learning based attack model, which can learn from attack history and launch attacks more efficiently. In experiments, we evaluate our model by attacking several state-of-the-art models on the benchmark datasets of multiple tasks including sentiment analysis, text classification and natural language inference. Experimental results demonstrate that our model consistently achieves both better attack performance and higher efficiency than recently proposed baseline methods. We also find our attack model can bring more robustness improvement to the victim model by adversarial training. All the code and data of this paper will be made public.

OpenAttack: An Open-source Textual Adversarial Attack Toolkit

Textual adversarial attacking has received wide and increasing attention in recent years. Various attack models have been proposed, which are enormously distinct and implemented with different programming frameworks and settings. These facts hinder quick utilization and apt comparison of attack models. In this paper, we present an open-source textual adversarial attack toolkit named OpenAttack. It currently builds in 12 typical attack models that cover all the attack types. Its highly inclusive modular design not only supports quick utilization of existing attack models, but also enables great flexibility and extensibility. OpenAttack has broad uses including comparing and evaluating attack models, measuring robustness of a victim model, assisting in developing new attack models, and adversarial training. Source code, built-in models and documentation can be obtained at https://github.com/thunlp/OpenAttack.

Textual Adversarial Attack as Combinatorial Optimization

Adversarial attack is carried out to reveal the vulnerability of deep neural networks. Textual adversarial attack is challenging because text is discrete and any perturbation might bring big semantic change. Word substitution is a class of effective textual attack method and has been extensively explored. However, all existing word substitution-based attack methods suffer the problems of bad semantic preservation, insufficient adversarial examples or suboptimal attack results. In this paper, we formalize the word substitution-based attack as a combinatorial optimization problem. We also propose a novel attack model, which comprises a sememe-based word substitution strategy and the particle swarm optimization algorithm, to tackle the existing problems. In experiments, we evaluate our attack model on the sentiment analysis task. Experimental results demonstrate our model achieves higher attack success rates and less modification than the baseline methods. The ablation study also verifies the superiority of the two parts of our model over previous ones.

Open the Boxes of Words: Incorporating Sememes into Textual Adversarial Attack

Adversarial attack is carried out to reveal the vulnerability of deep neural networks. Word substitution is a class of effective adversarial textual attack method, which has been extensively explored. However, all existing studies utilize word embeddings or thesauruses to find substitutes. In this paper, we incorporate sememes, the minimum semantic units, into adversarial attack. We propose an efficient sememe-based word substitution strategy and integrate it into a genetic attack algorithm. In experiments, we employ our attack method to attack LSTM and BERT on both Chinese and English sentiment analysis as well as natural language inference benchmark datasets. Experimental results demonstrate our model achieves better attack success rates and less modification than the baseline methods based on word embedding or synonym. Furthermore, we find our attack model can bring more robustness enhancement to the target model with adversarial training.