DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911200967655424 |
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| author | Dalal, Dwip Vashishtha, Gautam Rani, Anku Reganti, Aishwarya Patwa, Parth Sarique, Mohd Gupta, Chandan Nath, Keshav Reddy, Viswanatha Jain, Vinija Chadha, Aman Das, Amitava Sheth, Amit Ekbal, Asif |
| author_facet | Dalal, Dwip Vashishtha, Gautam Rani, Anku Reganti, Aishwarya Patwa, Parth Sarique, Mohd Gupta, Chandan Nath, Keshav Reddy, Viswanatha Jain, Vinija Chadha, Aman Das, Amitava Sheth, Amit Ekbal, Asif |
| contents | The rise in harmful online content not only distorts public discourse but also poses significant challenges to maintaining a healthy digital environment. In response to this, we introduce a multimodal dataset uniquely crafted for identifying hate in digital content. Central to our methodology is the innovative application of watermarked, stability-enhanced, stable diffusion techniques combined with the Digital Attention Analysis Module (DAAM). This combination is instrumental in pinpointing the hateful elements within images, thereby generating detailed hate attention maps, which are used to blur these regions from the image, thereby removing the hateful sections of the image. We release this data set as a part of the dehate shared task. This paper also describes the details of the shared task. Furthermore, we present DeHater, a vision-language model designed for multimodal dehatification tasks. Our approach sets a new standard in AI-driven image hate detection given textual prompts, contributing to the development of more ethical AI applications in social media. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21787 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images Dalal, Dwip Vashishtha, Gautam Rani, Anku Reganti, Aishwarya Patwa, Parth Sarique, Mohd Gupta, Chandan Nath, Keshav Reddy, Viswanatha Jain, Vinija Chadha, Aman Das, Amitava Sheth, Amit Ekbal, Asif Computer Vision and Pattern Recognition Computation and Language The rise in harmful online content not only distorts public discourse but also poses significant challenges to maintaining a healthy digital environment. In response to this, we introduce a multimodal dataset uniquely crafted for identifying hate in digital content. Central to our methodology is the innovative application of watermarked, stability-enhanced, stable diffusion techniques combined with the Digital Attention Analysis Module (DAAM). This combination is instrumental in pinpointing the hateful elements within images, thereby generating detailed hate attention maps, which are used to blur these regions from the image, thereby removing the hateful sections of the image. We release this data set as a part of the dehate shared task. This paper also describes the details of the shared task. Furthermore, we present DeHater, a vision-language model designed for multimodal dehatification tasks. Our approach sets a new standard in AI-driven image hate detection given textual prompts, contributing to the development of more ethical AI applications in social media. |
| title | DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images |
| topic | Computer Vision and Pattern Recognition Computation and Language |
| url | https://arxiv.org/abs/2509.21787 |