Video surveillance systems play an important role in maintaining security in smart city environments. In this context, person identification (Re-ID) systems based on deep learning are currently drawing substantial academic interest. However, these systems remain vulnerable to adversarial attacks. In existing methods, several attacks against Re-ID systems have been designed; nevertheless, they operate in the spatial domain. Existing attacks often suffer from perturbation visibility and low imperceptibility, making them easily detectable by human observers or automated detection systems. From this line of research, this study proposed a novel and potent alternative by designing frequency domain attacks, namely FreqAdv-FFT, FreqAdv-Wavelet, FreqAdv-Phase, FreqAdv-SelDCT, and FreqAdv-RandDCT. The frequency domain allows perturbations to be constructed in a way that utilizes the individual’s visual system’s decreased sensitivity to specific frequency ranges, making these perturbations less obvious. The proposed adversarial attacks were evaluated on two prominent datasets, Market-1501 and WB_WoB-ReID, across multiple models and attack variants. The highest performance degradation was observed with FreqAdv Wavelet on HRNet for the WB_WoB-ReID dataset, reducing the mean Average Precision (mAP) to 2.52%, and FreqAdv FFT on ResNet-50 for the Market-1501 dataset, achieving a mAP of 3.96%. The suggested attacks provide insights into establishing strong AI models as well as designing defenses for ReID-based surveillance systems that are relevant to the rising development of next-generation real-time applications.

The increased accessibility of social networking services (SNSs) has facilitated communication and information sharing among users. However, it has also heightened concerns about digital safety, particularly for children and adolescents who are increasingly exposed to online grooming crimes. Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims. However, research on grooming detection in South Korea remains limited, as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations, leading to inaccurate classifications. To address these issues, this study proposes a novel framework that integrates optical character recognition (OCR) technology with KcELECTRA, a deep learning-based natural language processing (NLP) model that shows excellent performance in processing the colloquial Korean language. In the proposed framework, the KcELECTRA model is fine-tuned by an extensive dataset, including Korean social media conversations, Korean ethical verification data from AI-Hub, and Korean hate speech data from HuggingFace, to enable more accurate classification of text extracted from social media conversation images. Experimental results show that the proposed framework achieves an accuracy of 0.953, outperforming existing transformer-based models. Furthermore, OCR technology shows high accuracy in extracting text from images, demonstrating that the proposed framework is effective for online grooming detection. The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.