Publications

@article{gangwar_deephsar_2024,

title = {DeepHSAR: Semi-supervised fine-grained learning for multi-label human sexual activity recognition},

author = {Abhishek Gangwar and Víctor González-Castro and Enrique Alegre and Eduardo Fidalgo and Alicia Martínez-Mendoza},

url = {https://www.sciencedirect.com/science/article/pii/S0306457324001596},

year  = {2024},

date = {2024-01-01},

journal = {Information Processing & Management},

volume = {61},

number = {5},

pages = {103800},

abstract = {This paper presents DeepHSAR, a deep learning framework for semi-supervised Human Sexual Activity Recognition (HSAR), using the SexualActs-150k dataset with 150k images. It employs two classification streams for global and fine-grained recognition, achieving an F1-score of 79.29%. The method outperforms previous approaches and achieves 99.85% accuracy on the NPDI Pornography-2k dataset.},

note = {Publisher: Pergamon},

keywords = {Fine-grained Classification, Multi-label Classification, pornography detection, Semi-supervised Classification, Sexual Activity Detection},

pubstate = {published},

tppubtype = {article}

}

@article{gangwar_attm-cnn_2021,

title = {AttM-CNN: Attention and metric learning based CNN for pornography, age and Child Sexual Abuse (CSA) Detection in images},

author = {Abhishek Gangwar and Víctor González Castro and Enrique Alegre and Eduardo Fidalgo},

url = {https://www.sciencedirect.com/science/article/pii/S092523122100312X},

year  = {2021},

date = {2021-01-01},

journal = {Neurocomputing},

volume = {445},

pages = {81–104},

abstract = {This paper proposes AttM-CNN, a deep learning model for detecting Child Sexual Abuse (CSA) material by combining pornographic content detection and age-group classification. Two new datasets, Pornographic-2M and Juvenile-80k, are introduced for training. The model outperforms state-of-the-art methods and improves CSA detection accuracy over forensic tools, aiding law enforcement.},

note = {Publisher: Elsevier},

keywords = {age-group detection, CNN, convolutional neural network, CSA detection, metric learning, pornography detection, visual attention},

pubstate = {published},

tppubtype = {article}

}

@article{gangwar_pornography_2017,

title = {Pornography and child sexual abuse detection in image and video: A comparative evaluation},

author = {Abhishek Gangwar and Eduardo Fidalgo and Enrique Alegre and Víctor González-Castro},

url = {https://digital-library.theiet.org/doi/10.1049/ic.2017.0046},

year  = {2017},

date = {2017-01-01},

abstract = {This paper reviews automatic detection methods for pornography and Child Sex Abuse (CSA) material, particularly in sensitive environments like educational or work settings. It evaluates five pornography detection approaches, including traditional skin detection and modern deep learning techniques, using two publicly available pornographic databases. The study finds that methods utilizing multiple features perform better than those relying on single features and that deep learning-based methods outperform traditional approaches, achieving state-of-the-art results. Additionally, the methods were tested on real-world CSA material provided by the Spanish Police.},

note = {Publisher: IET Digital Library},

keywords = {CSA, deep learning, Image classification, pornography detection},

pubstate = {published},

tppubtype = {article}

}