The proposed CLIP-based techniques can be extended to other types of machine learning models beyond image classification by adapting the methodology to suit the specific characteristics of the new domains. For natural language processing (NLP), the CLIP model can be fine-tuned on text data to learn semantic representations of words and sentences. This can enable the generation of trigger phrases or sentences that can be used to inject backdoors into NLP models. By leveraging the contextual understanding of CLIP, the generated triggers can be designed to blend seamlessly with the text data, making them harder to detect.
Similarly, for speech recognition tasks, the CLIP model can be utilized to extract features from audio data. By incorporating the audio embeddings into the backdoor injection process, it is possible to create audio triggers that can activate the backdoor in speech recognition models. The key lies in understanding the unique characteristics of the data in each domain and leveraging the pre-trained CLIP model to generate effective triggers that exploit vulnerabilities in the target models.
In essence, the CLIP-based techniques can be extended to various machine learning models by adapting the trigger generation process to the specific data modalities and characteristics of the target tasks, whether it be text, audio, or other forms of data.