Video-Based Sexual Harassment Detection Using Fine-Tuned Video Transformers

Jubayer- Al- Mahmud; Md Shamimul Islam; Md Nur Hossain

cs.CVOct 11, 2026

Video-Based Sexual Harassment Detection Using Fine-Tuned Video Transformers

Authors: Jubayer- Al- Mahmud, Md Shamimul Islam, Md Nur Hossain

Organizations: Department of CSE, Manarat International University, Dhaka, Bangladesh · Department of Computer Science, Florida Polytechnic University, Lakeland, Florida, USA

Abstract

Sexual harassment represents a pervasive societal challenge, with a substantial proportion of incidents remaining unreported due to insufficient evidence and delayed intervention mechanisms. Smart surveillance systems capable of detecting such incidents in real-time could assist law enforcement and serve as a deterrent. However, video-based harassment detection poses significant challenges, including variations in clothing, illumina- tion, partial occlusions, low resolution, and diverse camera view- points. Despite its social importance, this problem has received limited research attention, primarily due to the scarcity of large- scale annotated video datasets. To address this gap, we extend the Sexual Harassment Video (SHV) dataset to 600 short video clips sourced from YouTube and annotated into harassment and non-harassment categories using consistent labeling guidelines. We fine-tune three state-of-the-art video transformer models, VideoMAE, ViViT, and TimeSformer, pretrained on Kinetics- 400, for binary harassment classification. Experimental results show that all three models achieve competitive classification performance, with TimeSformer attaining the highest test accu- racy of 92.27%, outperforming VideoMAE 91.71% and ViViT 91.16% on the extended SHV test set. Our analysis reveals that performance is notably influenced by dataset size, video quality, frame sampling strategy, and model architecture. This work presents a comprehensive evaluation of modern video transformers for this sensitive real-world task.

Figures & tables

Fig. 1: Overview of the data preparation and model initialization pipeline for sexual harassment video classification.

Fig. 2: Example of 16 uniformly sampled frames extracted from a harassment video clip in the SHV dataset.

Description	Details
Total Classes	2
Total Videos	600
Harassment Videos	282
Non-Harassment Videos	318
Training Set (Harassment)	197
Training Set (Non-Harassment)	222

TABLE I: Sexual Harassment Video (SHV) Dataset Statistics

Configuration	VideoMAE	ViViT	TimeSformer
Pretrained Checkpoint	MCG-NJU/videomae-base-finetuned-kinetics	google/vivit-b-16x2-kinetics400	facebook/timesformer-base-finetuned-k400
Image Size	224	224	224
Num. Frames	16	32	8
Patch Size	16	16 $\times$ 2	16
Hidden Size	768	768	768
Num. Attention Heads	12	12	12

TABLE II: Comparison of video transformer model architectures and their configurations used in this study.

Fig. 3: Architecture overview of the three video transformer models used in this study: (a) VideoMAE, (b) ViViT, and (c) TimeSformer.

Model	Class	Test Loss	TP	FP	FN	TN	Precision	Recall	F1-Score	Macro F1
VideoMAE	Harassment	0.2601	88	8	7	78	0.907	0.918	0.912	0.917
	Non-Harassment		78	7	8	88	0.926	0.917	0.921
ViViT	Harassment	0.2474	93	3	13	72	0.960	0.847	0.900	0.910
	Non-Harassment		72	13	3	93	0.877	0.969	0.921
TimeSformer	Harassment	0.2079	94	2	12	73	0.973	0.859	0.912	0.922
	Non-Harassment		73	12	2	94	0.887	0.979	0.931

TABLE III: Per-Class and Average Performance of Fine-Tuned Video Transformer Models on the SHV Test Set.

Fig. 4: Test loss and accuracy comparison.

Hyperparameter	Value
Batch Size	2
Max Epochs	20
Initial Learning Rate	$10^{-5}$
Weight Decay	$10^{-4}$
Optimizer	AdamW
Loss Function	Cross-Entropy Loss

TABLE IV: Training Hyperparameters for Fine-Tuning

Model	Accuracy (%)
VideoMAE	91.71
ViViT	91.16
TimeSformer	92.27
CNN-LSTM [ 1 ]	70

TABLE V: Accuracy comparison on the SHV test set.

Video Content Moderation

Video content moderation detects and explains harmful, misleading, or policy-violating material in videos, including its visual, audio, and temporal context.

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HarassGuard: Detecting Harassment Behaviors in Social Virtual Reality with Vision-Language Models

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Kwangwoon University · ETRI · Chungbuk National University

Virtual Reality

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Social Virtual Reality (VR) platforms provide immersive social experiences but also expose users to serious risks of online harassment. Existing safety measures are largely reactive, while proactive solutions that detect harassment behavior during an incident often depend on sensitive biometric data, raising privacy concerns. In this paper, we present HarassGuard, a vision-language model (VLM) based system that detects physical harassment in social VR using only visual input. We construct an IRB-approved harassment vision dataset, apply prompt engineering, and fine-tune VLMs to detect harassment behavior by considering contextual information in social VR. Experimental results demonstrate that HarassGuard achieves competitive performance compared to state-of-the-art baselines (i.e., LSTM/CNN, Transformer), reaching an accuracy of up to 88.09% in binary classification and 68.85% in multi-class classification. Notably, HarassGuard matches these baselines while using significantly fewer fine-tuning samples (200 vs. 1,115), offering unique advantages in contextual reasoning and privacy-preserving detection.

Abstract

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