# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 5 Results and Discussion
### Robustness Analysis  
To comprehensively evaluate the stability and reliability of our model in practical applications, we designed robustness testing experiments from two dimensions: noise tolerance and adversarial attack resistance. The noise tolerance test employs four typical noise types (text synthesis, image Gaussian blur, comment injection, and combined noise) to assess model performance degradation under different intensities from 10 dB to 50 dB. The adversarial attack test designed four attack strategies (text perturbation, image adversarial, comment manipulation, and multimodal attack) and validated the effectiveness of the UC-CMAF defense mechanism. (1) Noise Tolerance Evaluation  
The results presented in Table 7 demonstrate that our system exhibits good robustness against various noise types, maintaining satisfactory performance even under enhanced noise conditions. Under extreme 50 dB noise environments, image Gaussian blur has the minimal impact (only 4.7% degradation), while combined noise shows the most significant effect (6.4% degradation), indicating that the superposition effect of multi-source noise causes greater impact on system performance. Overall, performance degradation under all noise types is controlled within acceptable ranges, proving that the model possesses good anti-interference capabilities. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Noise Type** & **Clean Acc.** & **10 dB** & **20 dB** & **30 dB** & **50 dB** \\ \hline Text Synthesis & 0.927 & 0.921 & 0.913 & 0.904 & 0.887 \\ Image Gaussian Blur & 0.927 & 0.924 & 0.920 & 0.914 & 0.896 \\ Comment Injection & 0.927 & 0.923 & 0.918 & 0.912 & 0.891 \\ Combined Noise & 0.927 & 0.918 & 0.906 & 0.892 & 0.868 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Robustness against different noise types. Figure 14: Comment–text attention alignment and importance score distribution. (2) Adversarial Attack Resistance  
UC-CMAF demonstrates strong resistance against adversarial attacks, particularly excelling in defending against multimodal attack methods, where single-modal attacks can be identified through inconsistency detection with other modalities. The experimental results presented in Table 8 show that image adversarial attacks have the lowest original success rate (8.7%), and after defense, the system accuracy can still maintain 91.3%, demonstrating the best defense effectiveness. In contrast, multimodal coordinated attacks represent the most challenging attack method with an original success rate of 18.9%, but the UC-CMAF defense mechanism can still control it within an acceptable range, achieving 81.1% accuracy after defense. To comprehensively evaluate the robustness of the model under complex environmental conditions, Figure 15a, Figure 15b, and Figure 15c respectively illustrate the model's accuracy under varying noise intensities, performance degradation under different adversarial attack strategies, and a heatmap showing key robustness metrics for each attack type. Figure 15a presents the accuracy trend under different types of noise, with the x-axis representing noise intensity (in dB) and the y-axis representing classification accuracy. It can be observed that as the noise intensity increases, the model performance degrades gradually, but the overall decline is relatively small, indicating strong noise robustness. Among the noise types, the model shows the highest tolerance to image Gaussian blur, while combined noise (multi-source) has the most significant impact. Figure 15b displays, in the form of a stacked bar chart, the attack success rate, accuracy drop, and defense effectiveness under four types of adversarial attacks. The results show that the model performs most robustly against image-based adversarial attacks, maintaining over 91% accuracy after defense. Although multimod  
Figure 15: Robustness evaluation results under noisy and adversarial conditions. (**a**) Accuracy under different noise intensities. (**b**) Performance under various adversarial attack types.