# Task-Oriented Adversarial Attacks for Aspect-Based Sentiment Analysis Models
## 6 Experimental Results and Analysis
Based on the observed negative impact on model performance (Table 1) and the quality of adversarial examples (Table 2), it is evident that modifying terms that support aspect-based analysis is crucial, since even simple modifications, such as replacing a word with its synonym, can effectively mislead the model. Through the gray-box attack, we evaluated the context independence of our approach by conducting experiments using the aspect-based Restaurant dataset. Specifically, we analyzed the model's accuracy before and after the attack to determine whether the negative impact of our method persists when applied across different domains. The results of the gray-box attack demonstrate the generalizability and context independence of the ABAA attack, as it consistently reduced model accuracy without requiring additional adjustments. By comparing results from white-box and gray-box attacks across different domain datasets, we reinforce the effectiveness of particularizing adversarial example designs to the specific characteristics of the targeted task. Regarding the transferability evaluation, we utilized the black-box attack to observe the feasibility of our approach when it is applied to different ABSA classifiers, in order to maintain its negative impact on the accuracy of the model. Transferability is a critical attribute for adversarial methods, demonstrating their robustness when adapted from one model to another. Until now, the lack of methods designed to address tasks in a \begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Opinion** & **Aspect** & **\%SS** & **\%PR** & **ATC** \\ \hline original & it was truly a great computer costing less than one thousand bucks before tax & costing & - & - & - \\ baseline 1 & it was **genuinely** a great computer **cost** less than one **grand dollar** before **taxation** & & 93.5 & 37.5 & Yes \\ ABAA & it was truly a great computer costing less than one thousand bucks before **taxation** & & 99.6 & 7.14 & No \\ \hline original & boots up fast and runs great & boots up & - & - & - \\ baseline 1 & **boot** up **quick** and runs great & & 96.5 & 33.33 & Yes \\ ABAA & boots up **quick** and runs great & & 98.7 & 16.66 & No \\ \hline original & the service i received from toshiba went above and beyond the call of duty & services & - & - & - \\ baseline 1 & the **assistance** i receive from toshiba **last supra** and beyond the **phone** & & 93.8 & 35.71 & Yes \\  & of **responsibility** & & 98.6 & 21.42 & No \\ ABAA & the service i receive from toshiba **last** above and beyond the **phone** & & 98.6 & 21.42 & No \\ \hline original & it has good speed and plenty of hard drive space & speed & - & - & - \\ baseline 1 & it has good **race** and **lot** of hard drive **place** & & 90.7 & 40.00 & Yes \\ ABAA & it has good speed and plenty of hard drive **capacity** & & 99.3 & 10.00 & No \\ \hline original & excellent speed for processing data & speed & - & - & - \\ baseline 1 & **splendid race** for **process information** & & 84.9 & 80.00 & Yes \\ ABAA & excellent speed for processing **information** & & 96.9 & 20.00 & No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adversarial examples generated from baseline 1 and ABAA attack. For each adversarial example \(x^{\prime}\), we present semantic similarity (SS), perturbation ratio (PR), and if there is any aspect term change (ATC). The modified terms are also indicated in bold according to the original sentence. particularized manner has avoided the effective transfer of attacks across models, even when they target the same task. However, the results of the black-box attack reveal that our approach successfully enables attack transferability. This success is attributed to the ABAA model's focus on the characteristics of the specific task to guide modifications, rather than relying on the victim model's operational details. Consequently, the ABAA attack can be seamlessly transferred without necessitating additional adjustments.