Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

Abstract: Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs). However, most existing works focus on attacking the image classification problem since its input space is continuous and output space is finite. In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities. To address the challenges caused by the discrete input space, we propose a projected gradient method combined with group lasso and gradient regularization. To handle the almost infinite output space, we design some novel loss functions to conduct non-overlapping attack and targeted keyword attack. We apply our algorithm to machine translation and text summarization tasks, and verify the effectiveness of the proposed algorithm: by changing less than 3 words, we can make seq2seq model to produce desired outputs with high success rates. On the other hand, we recognize that, compared with the well-evaluated CNN-based classifiers, seq2seq models are intrinsically more robust to adversarial attacks.

• The paper presents an optimization-driven framework that minimally modifies inputs to cause significant changes in seq2seq outputs.
• It proposes novel loss functions for non-overlapping and targeted keyword attacks tailored to the discrete nature of language.
• Experimental results demonstrate high success rates with minimal word modifications, highlighting the resilience of seq2seq architectures.

A Robustness Evaluation Framework for Sequence-to-Sequence Models: An Overview of Seq2Sick

The paper entitled "Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples" introduces a methodological framework for investigating the robustness of sequence-to-sequence (seq2seq) models against adversarial inputs. This research is founded upon the need for a deeper understanding of neural network vulnerabilities to adversarial attacks, which have been predominantly explored in the context of image classification tasks. In contrast, the authors address the more intricate problems posed by linguistically motivated models, focusing on machine translation and text summarization tasks.

Technical Contributions

The paper articulates a novel approach leveraging adversarial attack methodologies specifically adapted for seq2seq models. The challenges specific to these models arise from their reliance on discrete text inputs and the expansive nature of their output spaces, leading to distinct complexities in crafting adversarial examples compared to traditional image classification models.

1. Optimization-based Framework: The core of the proposed Seq2Sick framework is an optimization-driven approach designed to generate adversarial inputs that are minimally altered from the original while dramatically altering the model's output. The authors employ a projected gradient method combined with group lasso and gradient regularization to cope with the discrete input space.
2. Novel Loss Functions: To navigate the immense output space of seq2seq models, the authors propose custom loss functions for two primary attack strategies: non-overlapping attack and targeted keyword attack. These attacks aim to produce outputs either completely different from the original or containing specific targeted keywords, respectively.
3. Effective Results: Experimental evaluation demonstrates the capability of the Seq2Sick algorithm by achieving high success rates with minimal input modifications—typically less than three words changed—across several seq2seq models. These results are notably resilient, manifesting little semantic disturbance as verified through complementary sentiment classification assessments.

Implications and Considerations

The investigation reveals that despite the strong performance of Seq2Sick in generating adversarial examples, seq2seq models inherently possess a degree of robustness attributed to their handling of discrete inputs and extensive outputs. This intrinsic stability contrasts with convolutional neural network (CNN)-based classifiers, which are substantially more susceptible to adversarial vulnerabilities.

From a theoretical standpoint, these findings suggest a nuanced understanding of model robustness that hinges both on input dimensionality and output diversity. Practically, these insights highlight the importance of adversarial training and testing specifically tailored to seq2seq tasks to enhance model reliability, especially in applications like autonomous translation and secure content summarization where accuracy is critical.

Speculations on Future Directions

Given the demonstrated effectiveness of the Seq2Sick framework, future research may pivot towards integrating these findings to bolster seq2seq architectures, for instance, by coupling adversarial training with transformer-based models, which have shown promise in increasing model resilience. Moreover, extending the framework to account for more complex linguistic phenomena, like grammar and punctuation adversarial manipulation, may help model builders further insulate seq2seq systems against adversarial threats.

In summary, the Seq2Sick work represents a sophisticated milestone in adversarial machine learning, focused on language processing models, paving the way for enhanced comprehension and fortification of seq2seq systems against malicious inputs.