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ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations

Yunze Xiao1,Yujia Hu2, Kenny Tsu Wei Choo2, Roy Ka-wei Lee2
1Carnegie Mellon University  2Singapore University of Technology and Design
yunzex@andrew.cmu.edu
{yujia_hu, kenny_choo, roy_lee}@sutd.edu.sg
Abstract

Detecting hate speech and offensive language is essential for maintaining a safe and respectful digital environment. This study examines the limitations of state-of-the-art large language models (LLMs) in identifying offensive content within systematically perturbed data, with a focus on Chinese, a language particularly susceptible to such perturbations. We introduce ToxiCloakCN, an enhanced dataset derived from ToxiCN, augmented with homophonic substitutions and emoji transformations, to test the robustness of LLMs against these cloaking perturbations. Our findings reveal that existing models significantly underperform in detecting offensive content when these perturbations are applied. We provide an in-depth analysis of how different types of offensive content are affected by these perturbations and explore the alignment between human and model explanations of offensiveness. Our work highlights the urgent need for more advanced techniques in offensive language detection to combat the evolving tactics used to evade detection mechanisms.

ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations


Yunze Xiao1,Yujia Hu2, Kenny Tsu Wei Choo2, Roy Ka-wei Lee2 1Carnegie Mellon University  2Singapore University of Technology and Design yunzex@andrew.cmu.edu {yujia_hu, kenny_choo, roy_lee}@sutd.edu.sg


Disclaimer: This paper describes violent and discriminatory content that may be disturbing to some readers.

1 Introduction

Offensive language, which includes hate speech, cyberbullying, and adult-oriented content, poses significant risks to user well-being and social harmony Davidson et al. (2019). With the rapid expansion and widespread usage of social media platforms, the proliferation of offensive language has become a critical issue. Consequently, social media platforms and researchers have explored developing robust machine learning and linguistic analysis solutions to effectively identify and mitigate the harmful effects of offensive content Davidson et al. (2017); Dhanya and Balakrishnan (2021).

Recent advances in Natural Language Processing (NLP), particularly with Large Language Models (LLMs), have significantly improved the ability to detect offensive language across multiple languages Pitsilis et al. (2018); Wei et al. (2021); Fatemah and Ozlem (2021); Battistelli et al. (2020); Beyhan et al. (2022); Dhanya and Balakrishnan (2021); Deng et al. (2022a); Zhou et al. (2023). However, these models often struggle with systematically perturbed data designed to evade detection mechanisms. Common perturbation techniques include homophonic substitutions, emoji replacement, insertions, character splits, and synonyms Su et al. (2022); Kirk et al. (2022). These techniques, referred to as ”cloaking”, exploit linguistic nuances to mask offensive content, posing a substantial challenge to both automated systems and human moderators.

The Chinese language, in particular, is heavily impacted by these techniques due to intensive lexicon-based censorship, leading to a new linguistic phenomenon Wiener (2011) where significant parts of sentences are replaced by either homophones or emojis to mask underlying offensive content or to circumvent censorship rules. Figure 1 shows two examples of offensive texts cloaked using homophone and emoji replacement techniques. In these examples, the words and phrases highlighted in yellow are replaced with homophones or emojis. In the first example, homophones are used to replace phrases that identify the target (e.g., “贺楠仁” as the homophone for “河南人,” which means people from the Henan region in China) and offensive terms such as “太贱” with “肽键.” Similarly, in the second example, the offensive term “舔狗” (i.e., Simps) is replaced with [Uncaptioned image][Uncaptioned image]. Using such techniques, users can fool automated offensive language detectors into misclassifying these sentences as non-offensive, even though avid Chinese social media users will have no problem understanding the offensive context of the text. Addressing this problem is crucial to improve the effectiveness of offensive language detection systems. As these evasion techniques evolve, it becomes increasingly important for these offensive langauge detection systems to adapt and accurately identify cloaked offensive content.

Refer to caption
Figure 1: Example of cloaked Chinese offensive language using homophone and emoji replacement. By using such techniques, users will be able to fool the automated offensive language detector into misclassifying them as normal sentences.

In this work, we introduce ToxiCloakCN, a novel Chinese offensive content dataset that benchmark content moderation models’ ability to detect offensive texts cloaked using homophone and emoji replacements. Specifically, we conduct extensive experiments and evaluate state-of-the-art LLMs on the ToxiCloakCN dataset. The experiments demonstrated that both perturbation methods significantly affect the models’ capabilities in detecting offensive text. We also analyze the effect of prompts on the experimental results by testing the models using six different prompts. Additionally, we analyze the perturbation effects on different types of offensive content: sexism, racism, regional bias, and anti-LGBTQ+. This research underscores the critical need for developing more robust models to effectively moderate cloaked online offensive content.

We summarize the main contributions of this paper as follows:

  • •

    We introduce ToxiCloakCN, a novel dataset specifically designed to evaluate the robustness of LLMs against homophonic and emoji perturbations, addressing a significant gap in current offensive language detection research.

  • •

    We conduct a comprehensive evaluation of state-of-the-art LLMs. Our experimental results reveal that leading LLMs struggle to detect cloaked offensive content, highlighting the limitations of current approaches and the need for more advanced detection techniques.

  • •

    We analyze how different types of offensive content are impacted by cloaking perturbations, providing critical insights for improving model robustness and effectiveness in real-world applications.

2 Related work

2.1 Chinese Offensive Content Dataset

Several datasets have been developed for Chinese offensive language detection. The Chinese Offensive Language Dataset (COLD) categorizes sentences into groups like individual attacks and anti-bias Deng et al. (2022a). TOCP and TOCAB from Taiwan’s PTT platform address profanity and abuse Chung and Lin (2021). The Sina Weibo Sexism Review (SWSR) focuses on sexism within Chinese social media Jiang et al. (2021). The ToxiCN dataset from platforms like Zhihu and Tieba includes a multi-level labeling system for offensive language, hate speech, and other categories Lu et al. (2023). In this work, we introduce ToxiCloakCN, a novel dataset capturing cloaked offensive text using homophonic and emoji replacements, built on top of the comprehensive ToxiCN dataset.

2.2 Chinese Offensive Content Detection

Offensive language and hate speech detection have been explored in various languages, including English Davidson et al. (2017); Pitsilis et al. (2018); Wei et al. (2021), Arabic Fatemah and Ozlem (2021), French Battistelli et al. (2020), Turkish Beyhan et al. (2022), and Asian languages Dhanya and Balakrishnan (2021). In Chinese, techniques include lexicon-based models Zhang et al. (2010); Deng et al. (2022b), supervised and adversarial learning models Jiang et al. (2021); Liu et al. (2020b), knowledge-based models Liu et al. (2020a), and fine-tuned pretrained models Deng et al. (2022a) like BERT Devlin et al. (2019). Cross-cultural transfer learning models also adapt to cultural differences Zhou et al. (2023). Nevertheless, existing models mainly focus on explicit offensive content. This work addresses the gap by evaluating models’ ability to detect cloaked offensive content.

2.3 Language Perturbation

Various perturbation techniques have been proposed to investigate the vulnerabilities of NLP models in adversarial scenarios. These include inserting emojis Kirk et al. (2022), token replacements and insertions Garg and Ramakrishnan (2020), and probability-based greedy replacements Ren et al. (2019). While these methods primarily target English, adapting them to Chinese is challenging due to linguistic differences, though some attempts have been made Liu et al. (2023).

For Chinese, Su et al. have highlighted adversarial attacks such as word perturbation, synonyms, and typos Su et al. (2022). Subsequent solutions have focused on BERT-based models to address these attacks Zhang et al. (2022); Wang et al. (2023); Xiong et al. (2024). However, previous work mainly evaluates BERT-based models and lacks robustness research on LLMs and social media-based adversarial datasets reflecting current trends. Our work addresses this gap by providing a new dataset with realistic perturbations for Chinese offensive language detection.

3 Methodology

The ToxiCloakCN dataset builds upon the ToxiCN dataset Lu et al. (2023) through a detailed multi-step process. First, we sampled a balanced dataset from the base ToxiCN dataset, known as the “base” dataset. Next, this balanced base dataset was perturbed using homophone and emoji replacements to produce the ToxiCloakCN dataset. After constructing the ToxiCloakCN dataset, we explored pinyin augmentation as a potential solution to address the “cloaked” offensive content perturbed using homophone replacements. Finally, we defined six different instructions for evaluating the performance of state-of-the-art large language models on ToxiCloakCN.

3.1 Dataset Construction

3.1.1 Sampling Base Dataset

The ToxiCN dataset was chosen as the foundational dataset due to its well-controlled annotation, with Fleiss Kappas for different granularities exceeding 0.6 Lu et al. (2023). We first collated the offensive lexicon (i.e., swearwords) identified in ToxiCN. Next, we sampled sentences from ToxiCN labeled as “offensive” or “hateful” that contained the offensive lexicon, resulting in 2,293 offensive sentences. To balance the dataset, we also sampled non-offensive sentences from ToxiCN, giving preference to sentences containing the offensive lexicon but labeled as “non-offensive”. In total, we sampled 2,289 non-offensive sentences. The combined 4,582 sampled sentences form the base dataset that we will use for perturbation.

Table 1 shows the base dataset distribution breakdown by content topics, mirroring the statistical distribution of the base ToxiCN dataset. It is worth noting that a substantial amount of offensive content in the base dataset pertains to racism, followed by sexism and anti-LGBTQ content. We have relatively lesser regional bias offensive content in the base dataset.

Topic All Non-Offensive Offensive
Race 1,769 872 897
Gender 1,229 546 683
LGBTQ+ 913 407 506
Region 671 464 207
Table 1: Base dataset distribution breakdown by content topics.
Instruction Template Inputs
English_Text Please decide if the following text is offensive or not. ‘0’ means not offensive, ‘1’ means offensive. {Text}
Chinese_Text 请判断下列句子是否是冒犯的？‘0’意味着不冒犯，‘1’意味着冒犯. {Text}
English_Pinyin Please determine if the following pinyin-expressed Chinese sentence is offensive. ‘0’ means not offensive, ‘1’ means offensive. {Pinyin}
Chinese_Pinyin 请判断下列拼音所表达的中文句子是否是冒犯的？‘0’意味着不冒犯，‘1’意味着冒犯. {Pinyin}
English_Text+Pinyin Please determine if the following text is offensive and make your judgment based on pinyin. ‘0’ means not offensive, ‘1’ means offensive. {Text} {Pinyin}
Chinese_Text+Pinyin 请结合拼音判断下列句子是否是冒犯的？‘0’意味着不冒犯，‘1’意味着冒犯. {Text} {Pinyin}
Table 2: Instructions used in prompting LLMs to detect offensive content in ToxiCloakCN.

3.1.2 Homophone Replacement

From the base dataset, we replaced keywords in each sentences in base dataset with their homophones, based on a specified dictionary111https://github.com/dongrixinyu/JioNLP/. Keywords were identified as those present in the specified dictionary. To ensure the quality of the texts, we kept the perturbation ratio at 0.3, meaning that 30% of individual characters in each sentence were replaced with homophones. Higher perturbation ratios made sentences difficult to understand, while lower ratios resulted in insufficient disruption. Therefore, a 0.3 ratio was chosen to ensure both visible perturbation and readability. The homophone-replaced sentences will be added into our new ToxiCloakCN dataset.

3.1.3 Emoji Replacement

Utilizing the advanced script proposed by Zhang (2024), we further performed emoji replacement on the sentences in the base dataset. Initially, text segmentation techniques were applied to identify key words within the sentences. These key words were then replaced with corresponding emojis from a predefined emoji lexicon dictionary222https://github.com/THUzhangga/NMSL. The perturbation rate was also set at 0.3. When a direct lexical match was unavailable in the dictionary, the algorithm applied a phonetic approximation method based on the word’s pinyin to achieve the replacement. For example, to convert the word ‘操 (Fu*k)’ to its emoji representation, the output would be ([Uncaptioned image]). Since there is no direct emoji match for ‘操 (Fu*k)’ in the dictionary, and ‘操 (Fu*k)’ and ‘草 (grass)’ are homophones, the emoji ([Uncaptioned image]) of homophonic ‘草 (grass)’ is chosen as the replacement. Finally, the emoji-replaced sentences are added to the ToxiCloakCN dataset.

3.2 Pinyin Augmentation

While we aim to benchmark the state-of-the-art LLMs’ ability to detect cloaked offensive content in our newly constructed ToxiCloakCN dataset, we also explore potential solutions to aid LLMs’ in the detection task. Specifically, we explore pinyin augmentation method as a potential solution to detect homophone-replaced offensive sentences in ToxiCloakCN. Pinyin is the official romanization system for Standard Mandarin Chinese in mainland China and Taiwan, using the Latin alphabet to represent Chinese characters phonetically. The intuition for this method is that, given the nature of homophones, the pinyin representation should look alike, if not the same, thus potentially helping the model identify the offensiveness. Both ToxiCN and ToxiCloakCN datasets theoretically share the same phonetic data, despite their textual differences. Therefore, we used the pypinyin333pypinyin package to derive pinyin of the sentences in ToxiCloakCN.

3.3 Instruction Templates

To observe the effect of prompting on the task, we propose six distinct instruction templates to verify the efficacy of our ToxiCloakCN dataset. These instructions are carefully designed to evaluate the effects of prompt languages (i.e., English and Chinese) on the offensive content detection task, as well as the effect of pinyin augmentation. Table 2 shows the six instructions designed and applied in our experiments.

Model Training Set Instruction Type Homophone Emoji Base
Lexicon-based ToxiCN Lexicon - 0.003 (0.297) - 0.300
COLDetector COLD - 0.582 (0.043) 0.615 (0.010) 0.625
English_text 0.637 (0.040) 0.634 (0.043) 0.677
Chinese_text 0.666 (0.023) 0.649 (0.040) 0.689
LLAMA-3-8B COLD English_pinyin 0.637 (0.000) - 0.637
Chinese_pinyin 0.634 (0.000) - 0.634
English_Text+Pinyin 0.637 (0.035) - 0.672
Chinese_text+Pinyin 0.638 (0.034) - 0.672
English_text 0.650 (0.043) 0.618 (0.075) 0.693
Chinese_text 0.669 (0.031) 0.640 (0.060) 0.700
Qwen COLD English_pinyin 0.630 (0.000) - 0.630
Chinese_pinyin 0.613 (0.000) - 0.613
English_Text+Pinyin 0.646 (0.048) - 0.694
Chinese_text+Pinyin 0.649 (0.051) - 0.700
English_text 0.650 (0.038) 0.631 (0.057) 0.688
Chinese_text 0.669 (0.022) 0.649 (0.042) 0.691
Mistral COLD English_pinyin 0.622 (0.000) - 0.622
Chinese_pinyin 0.613 (0.000) - 0.613
English_Text+Pinyin 0.649 (0.037) - 0.686
Chinese_text+Pinyin 0.651 (0.039) - 0.690
English_text 0.709 (0.055) 0.621 (0.143) 0.764
Chinese_text 0.727 (0.069) 0.754 (0.042) 0.796
GPT-4o English_pinyin 0.649 - 0.678
Chinese_pinyin 0.723 (0.018) - 0.741
N/A English_Text+Pinyin 0.719 (0.042) - 0.761
Chinese_Text+Pinyin 0.741 (0.022) - 0.763
Table 3: Macro F1 scores of benchmark models. Note that "Homophone" and "Emoji" denote the homophone-replaced and emoji-replaced sentences in the ToxiCloakCN dataset, respectively. Best performances are bolded. Values in () represent the difference between the Macro F1 score on the base dataset and the Homophone/Emoji datasets (i.e., performance decline).

4 Experiments

4.1 Baselines

Lexicon-based. We employed a lexicon-based detection method to identify offensive language, classifying text as offensive if it contained any words from the ToxiCN offensive lexicon, otherwise marking it as non-offensive Xiao et al. (2024); Lu et al. (2023).

COLDetector. We implemented COLDETECTOR Deng et al. (2022a), a BERT-based model for offensive language detection. This approach involves feeding the text into the BERT model, extracting the first hidden state from the final layer, and connecting it to a linear layer for the final prediction. The model is trained on the COLD dataset Deng et al. (2022a), a popular benchmark for Chinese offensive language detection.

Large Language Models. We evaluate GPT-4o and three open-source LLMs—LLaMA-3-8B AI@Meta (2024), Qwen1.5-MoE-A2.7B Team (2024), and Mistral-7B Jiang et al. (2023)—for the Chinese offensive language detection task. The open-source models were fine-tuned on the COLD training datasets using the six proposed instructions. Utilizing the LORA method Hu et al. (2021), we introduced 4.1 million additional parameters, which is only 0.06% of the total parameters. Fine-tuning was conducted over three epochs using the LLM-Adapters Toolkit Hu et al. (2023). GPt-4o and the fine-tuned models were then evaluated on the base and ToxiCloakCN datasets. All fine-tuning and inference phases are performed on two NVIDIA A6000 GPUs.

4.2 Evaluation Metric

To confirm with established research norms Deng et al. (2022a); Lu et al. (2023), we utilize Macro F1 score as the evaluation metrics for the offensive language detection task. The metric assess the models’ performance in classifying the offensive languages in the datasets.

4.3 Experimental Results

Table 3 presents the offensive detection outcomes for all models, showing that GPT-4o achieves the highest performance with Chinese-only text instructions. However, all models exhibit a notable performance decline on the homophone and emoji replaced sentences in ToxiCloakCN dataset compared to the base dataset. This indicates a significant reduction in their ability to detect offensive content when the text is perturbed. The drop in performance is primarily due to the probabilistic nature of LLMs, which rely on next-word prediction based on statistical probabilities. Perturbations like homophone and emoji replacements disrupt this probability chain, compromising the models’ ability to generate coherent and contextually accurate responses.

Refer to caption
Figure 2: The models’ error rates comparison between the sentences in the base dataset and the homophone or emoji-replaced sentences using prompt type Chinese_text , broken down by offensive content type. Note that smaller error rates represent better performance.

4.3.1 Effects of Pinyin Augmentation

When pinyin was added to the text, we observed a performance reduction across all models on the homophone-replaced sentences in ToxiCloakCN dataset compared to text-only inputs. Instead of enhancing model capabilities, pinyin disrupted performance, leading to lower classification accuracy. Pinyin-only input experiments consistently showed lower performance compared to text inputs, highlighting the models’ limitations in processing pinyin. This aligns with Li et al.’s finding that LLMs recognize pinyin but struggle with pronunciation, making it difficult to correct phonological errors. This limitation affects the models’ ability to detect offensive language significantly perturbed through homophone replacements. The performance drop with pinyin augmentation is partially due to pinyin not being directly associated with the original phrase, causing confusion for the model.

4.3.2 Effect of Instruction Templates

Across all models, Chinese instructions consistently outperformed English instructions for tasks involving Chinese text. Conversely, for pinyin-only tasks, English instructions yielded better results than Chinese instructions. This is likely due to the task characteristics: using Chinese instructions for Chinese text maintains linguistic consistency, enhancing the model’s understanding. For pinyin-only tasks, English instructions, which align better with the letter-based nature of pinyin, improved coherence and model performance.

1. Text: 整急言了我可以干猪，但是不毁感油郭黑人难有的女人
GPT-4o: Yes, that’s offensive, keywords: ‘fu*k pig(干猪)’, ‘ will fu*k(会干)’ ‘black(黑人)’, ‘woman(女人)’
Human Annotator: The phrase is offensive. The speaker is saying that he would rather fu*k pig (干猪) than having sex (毁感) with women that have a black boyfriend (油郭黑人难) - which is both sexist and racist.
2. Text: 我已经举报家爆啥这母钩乐
GPT-4o: “家爆” - Whilst it is correct to describe a situation of domestic violence, the direct use of such an acronym may not appear serious and respectful.‘母钩乐’ - - This is an insulting term that is a personal attack on another person and is clearly derogatory and abusive in nature. thus, the phrase is offensive.
Human Annotator: The phrase is offensive. The speaker is saying that he has reported (举报) and (家) roasted (爆啥) the bit*h (母钩乐).
3. Text: 奇趣蛋还是滚来滚去比较号
GPT-4o: Your reference to ‘奇趣蛋还是滚来滚去比较好’ sounds like a humorous description of something or a situation. If this is a reference to a Chick-fil-A toy (the common chocolate egg with small toys), it could be describing the fun of such toys, especially when they are rolled around.
Human Annotator: The phrase is offensive. The speaker is saying that overweight women (奇趣蛋) is better off (比较号) rolling around (滚来滚去). Note that 奇趣蛋 is not a homophone but a slur.
Table 4: Examples of offensive sentences with homophone replacements. The offensive text are shown in black. GPT-4o interpretation of the text are in red, and the human annotator’s explanations are in blue.
1. Text:告诉她 door, [Uncaptioned image]家[Uncaptioned image]栏的公[Uncaptioned image]最近发春, 吊又长又大, 是真的大, [Uncaptioned image]是怕片宣传挑出来的大, 送给她[Uncaptioned image]夜夜春宵, [Uncaptioned image], 没艾滋病
GPT-4o: Yes, the phrase is harmful because it contains sexual innuendo and inappropriate metaphors that could be perceived as disrespectful and sexually harassing. In addition, it incorrectly characterizes health conditions, which can lead to misunderstanding and discrimination against specific diseases.
Human Annotator: This phrase is offensive. The speaker is saying that his cowshed’s cow([Uncaptioned image]家[Uncaptioned image]栏的公[Uncaptioned image]) is horny(发春), and can give them for free to have sex(送给她[Uncaptioned image]夜夜春宵,[Uncaptioned image])
2. Text:归根究底，广大普通[Uncaptioned image]性若是靠近普[Uncaptioned image] [Uncaptioned image]，会[Uncaptioned image]幸。这是最正确的结论。
GPT-4o: Yes, this statement could be seen as offensive because it imposes gender roles and expectations, suggesting that women can only be happy if they are dependent on men, a view that could be considered sexist.
Human Annotator: Yes, this statement is offensive because it is sarcastically stating that all the women([Uncaptioned image]性) will be unfortunate (会[Uncaptioned image]幸) if they get closer to normal men (普[Uncaptioned image] [Uncaptioned image]). Note that 普信男(普[Uncaptioned image] [Uncaptioned image]) is a abbreviation that is derogatory towards men.
3. Text:[Uncaptioned image][Uncaptioned image] [Uncaptioned image] [Uncaptioned image] house
GPT-4o: The language consists of various emoticons and the English word ‘house’ and lacks clear context. In the absence of obvious harmful content, the passage itself is not directly harmful.
Human Annotator: The phrase is offensive. The speaker is saying that simps([Uncaptioned image][Uncaptioned image]) does not deserve a good death( [Uncaptioned image] [Uncaptioned image] house). Note that in this example, both homophones and emojis has been used.
Table 5: Examples of offensive sentences with emoji replacements. The offensive text are shown in black. GPT-4o interpretation of the text are in red, and the human annotator’s explanations are in blue.

4.4 Case Studies

The decline in Macro F1 scores across all models on ToxiCloakCN datasets highlights the challenges these models face in offensive detection tasks. In this section, we aim to empirically explore why LLMs struggle with offensive content with homophone or emoji replacements. Through case studies, we analyzed the effects of the perturbations on various types of offensive content. Additionally, we analyze specific cases to compare the differences between LLMs and human’s understanding of the cloaked offensive content.

4.4.1 Effects of Perturbation on Offensive Content Types

We examined the effects of homophone and emoji replacements on different offensive content types, namely racism, sexism, anti-LGBTQ+, and regional bias. Figure 2 depicts the models’ error rates on the base and ToxiCloakCN datasets across these content types. Note that the performance is based on the Chinese_Text instruction, and a smaller error rate indicates better performance in the offensive language detection task.

Generally, we observe that all models have lower error rates on the base dataset across all offensive content types, supporting our initial findings that LLMs struggle to detect cloaked offensive language, regardless of content type. Interestingly, for the open-source LLMs, we notice a smaller difference between the error rates for regional bias offensive content in the base and ToxiCloakCN datasets. This could be due to a generalization issue; the open-source LLMs are fine-tuned on COLD, which may not contain much content related to regional bias, resulting in poorer performance in detecting this type of offensive content, regardless of perturbation. However, for the closed-source model, GPT-4o, we observe performance gaps for regional bias offensive content when the sentences are perturbed using homophone and emoji replacements.

4.4.2 Comparison Between LLMs and Human Understanding of Cloaked Offensive Content

To explore the reasons behind the models’ poor performance on the ToxiCloakCN dataset, we conducted a detailed analysis using the top-performing GPT-4o model and human annotators, focusing on the comparison between human and model’s understanding of cloaked offensive content. Specifically, we randomly selected several offensive sentences from the ToxiCloakCN dataset and examined GPT-4o’s interpretation of these sentences. We recruited two human annotators—an undergraduate and a postgraduate student—who are proficient in Chinese and active users of Chinese social media. They assessed the offensiveness of the given sentences and provided detailed explanations. Through these case studies, we aim to compare GPT-4o’s understanding with human understanding of these cloaked offensive sentences and empirically identify any gaps in the model’s comprehension of cloaked offensive content.

Homophones. Table 4 presents three homophone-replaced offensive sentences from the ToxiCloakCN dataset. In the first example, GPT-4o correctly identifies the offensive content by recognizing keywords like ’干猪’ (’fu*k pig’). This suggests that GPT-4o has some understanding of homophones, enabling it to detect cloaked offensive language. In the second example, while the model correctly classifies the sentence as offensive, its explanation does not match the original meaning of the offensive sentence. For instance, it identifies ’母钩乐’ as offensive but cannot explain why. The human annotator, however, can reconstruct the sentence and provide an accurate judgment and explanation. In the third example, GPT-4o misjudges and misinterprets the phrase due to its inability to understand the cultural background. This example demonstrates the model’s limitation in recognizing implicit offensive language across different cultures, whereas human annotators, with their cultural understanding, can make accurate judgments.

Emoji. Table 5 presents three emoji-replaced offensive sentences from the ToxiCloakCN dataset. In the first example, both GPT-4o and the human annotator accurately identify the offensive content. This case is relatively simple because offensive keywords such as ‘吊’ (a homophone for ‘di*k’) and ‘艾滋病’ (AIDS) remain unchanged. In the second example, although the model classifies the sentence as offensive, its explanation differs from that of the human annotator, indicating a misinterpretation. This may be due to the model’s failure to grasp emoji meanings, such as ‘[Uncaptioned image]’ (which means ‘not’ in this context). The third example involves complex emoji and homophone replacement, with ‘simp’ translated to ‘舔狗’ in Chinese, represented by emojis for ‘舔’ (lick) and ‘狗’ (dog). ‘不得’ (not deserve) was replaced by an emoji ([Uncaptioned image][Uncaptioned image]) and the last two characters(好死) are phonetically converted to ‘house’ in English. GPT-4o misclassifies and misinterprets this complex content, whereas the human annotators are able identify it, highlighting the need for developing more robust solutions capable of handling such cloaked offensive languages.

5 Conclusion and Future Works

In this study, we explored the robustness of current Chinese offensive language detection models against cloaking perturbations, specifically homophone and emoji replacements. We developed the ToxiCloakCN dataset by augmenting the ToxiCN dataset with these perturbations to simulate real-world scenarios where users attempt to evade detection systems. Our experimental results demonstrated that the performance of state-of-the-art models, including GPT-4o, significantly declines when faced with these cloaked offensive content. Although our proposed pinyin augmentation method showed some promise, its effectiveness was limited across different models, highlighting the complexity of phonetic alignment in offensive language detection.

Our case studies further revealed notable gaps in the models’ understanding of cloaked offensive content compared to human annotators. GPT-4o often missed or misinterpreted offensive keywords disguised by homophones or emojis, while human evaluators, with their cultural and contextual understanding, could accurately identify the offensive nature of these texts. This emphasizes the need for models that can better mimic human comprehension of nuanced and contextually rich language. These findings underscore the urgent need for more advanced techniques to handle such evolving strategies.

Future research should expand cloaking techniques beyond homophone and emoji perturbations, incorporate a wider range of linguistic variations from real-life internet sources, and develop more sophisticated phonetic alignment methods to improve model robustness. Additionally, creating algorithms that integrate deeper semantic understanding and context-awareness wifll be essential for effectively handling cloaked offensive language. By building on the groundworks of this study and addressing these areas, future research can significantly advance the field of offensive language detection, contributing to safer and more respectful digital environments.

Limitation

This study has several limitations. Firstly, while our dataset includes comprehensive homophone and emoji perturbations, it may not encompass the entire range of adversarial techniques employed in real-world scenarios. This limitation could affect the generalizability of our findings to other perturbation forms not examined in this study. Additionally, our reliance on the ToxiCN dataset, despite its robustness, might not fully capture the diversity of offensive language across various Chinese dialects and regional linguistic nuances. This limitation could impact the broader applicability of our findings. Future research should consider subsampling perturbed data from real-life internet sources such as Tieba444https://tieba.baidu.com and NGA555https://nga.cn to gain a more accurate and timely understanding of these perturbed languages in real life. Lastly, our work does not provide a definitive solution for addressing all challenges related to cloaked offensive language detection. Future work should undertake more thorough and advanced analyses to develop effective solutions for these challenges

Ethical Statement

This research focuses on the detection of offensive language, particularly in the context of homophonic and emoji perturbations used to bypass detection mechanisms. Our primary goal is to highlight the vulnerabilities of current language models and enhance their robustness against these cloaking techniques, thereby contributing to safer and more respectful online environments.

The study involves using systematically perturbed data to test the limits of existing models. While this approach is crucial for understanding and improving detection capabilities, there are inherent risks associated with the potential misuse of these findings. Specifically, the techniques developed to detect cloaked offensive language might also be studied to refine evasion tactics further. However, it is important to emphasize that our work is solely aimed at detecting and mitigating offensive language, not to facilitate censorship or suppress free speech.

Our dataset and perturbations are derived from existing resources; no new data was collected for this study. The use of ToxiCloakCN aligns with the ToxiCN dataset’s intention, which states, "All resources are for scientific research only." We have also carefully adhered to the Apache-2.0 license used by JioNLP and the MIT license for pypinyin.

Our research is conducted with the explicit aim of improving the detection of offensive language. Our efforts are directed towards contributing positively to the broader field of content moderation, ensuring that online platforms can effectively manage offensive language while respecting the principles of free and open communication.

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