Mitigando os Efeitos de GAN em Classificação de Imagens com CNN
Resumo
A CNN (Convolutional Neural Network) tem sido frequentemente usada para solução de problemas, gerando um modelo que pode prever a classe da imagem. Neste trabalho, a ausência de integridade na CNN é verificada usando uma GAN (Generative Adversarial Network). Para isso, modelamos um classificador de autenticidade baseado no algoritmo NB (Naive Bayes). Quando os modelos NB e CNN propostos trabalham juntos, 88,88% de acerto foram alcançados. Em 89,88% dos casos as imagens fakes foram identificadas e descartadas. No caso específico da CNN, obteve-se uma precisão de 85,06% com uma confiança de 95%.Referências
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Goodfellow, I. J.; Pouget-Abadie, J.; Mirza M.; Xu, B.; Warde-Farley D.; Sherjil O.; Aaron C.; Yoshua B. (2014). "Generative Adversarial Nets." Neural Inf. Processing Systems (NIPS).
He, K.; Zhang, X.; Ren, S.; Sun, J. (2015). "Deep residual learning for image recognition". CoRR, abs/1512.03385, 2015.
Karamizadeh, S. e Arabsorkhi, A. (2018). "Methods of Pornography Detection: Review". In Proceedings of the 10th International Conference on Computer Modeling and Simulation (ICCMS 2018). ACM, New York, NY, USA, 33-38. DOI: https://doi.org/10.1145/3177457.3177484.
Krizhevsky, A.; Sutskever, I.; Hinton, G.E. (2012). "ImageNet classification with deep convolutional neural networks", In Advances in neural information processing systems, pages 1097–1105.
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Macedo, J.; Costa, F.; Santos, J.A. dos. (2018). "A Benchmark Methodology for Child Pornography Detection". 31st SIBGRAPI Conf. on Graphics, Patterns and Images (SIBGRAPI).
Moreira, D.; Avila, S.; Perez, M.; Moraes, D.; Testoni, V.; Valle, E.; Goldenstein, S.; Rocha, A. (2016). "Pornography classification: The hidden clues in video space- time", Forensic Science Int. Vol. 268, p. 46-61.
Norton, A.P.; Qi, Y. (2017). "Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning". IEEE Symposium on Visualization for Cyber Security (VizSec).
Oquab, M.; Bottou, L.; Laptev, I.; Sivic, J. (2014). "Learning and transferring mid-level image representations using convolutional neural networks" in Proc. of the IEEE conf. on computer vision and pattern recognition, pp. 1717–1724.
Polastro, M.C. e Eleuterio, P.M.S. (2010). "NuDetective: a Forensic Tool to Help Combat Child Pornography through Automatic Nudity Detection", Workshop on Database and Expert Systems Applications (DEXA).
Ponti, A. M.; Ribeiro, L.S.F.; Nazare, T.S.; Bui, T.; Collomosse, J. (2017). "Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask", 30th SIBGRAPI Conf. on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), Niterói, 2017, pp. 17-41.
Sae-Bae, N.; Sun, X.; Sencar, H.T.; Memon, N.D. (2014). "Towards automatic detection of child pornography", In: The 2014 IEEE Int. Conf. on Image Processing (ICIP), pages 5332–5336.
Sebastiani, F. "Machine Learning in Automated Text Categorization", ACM Computing Surveys, Vol. 34. No. 1, Março 2002, p.1-47.
Simonyan, k.; e Zisserman, A. (2014). "Very deep convolutional networks for large- scale image recognition," CoRR, vol. abs/1409.1556, 2014.
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. (2016). "Rethinking the inception architecture for computer vision". In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pages 2818–2826, 2016.
Ulges, A.; e Stahl, A. (2011). "Automatic detection of child pornography using color visual words," in Multimedia and Expo (ICME), 2011 IEEE Int. Conf. on. IEEE, 2011, pp. 1–6.
Vitorino, P.; Avila, S.; Rocha, A. (2016). "A Two-tier Image Representation Approach to Detecting Child Pornography". Proc. of XII Workshop de Visão Computacional.
Vitorino, P.; Avila, S.; Perez, M.; Rocha, A. (2018). "Leveraging deep neural networks to ght child pornography in the age of social media". Journal of Visual Communication and Image Representation (50).
Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J. (2016). "Practical Machine Learning Tools and Techniques", Editora Morgan Kaufmann. 4th Edition.
Yiallourou, E.; Demetriou, R.; Lanitis, A. (2017). "On the Detection of Images Containing Child-Pornographic Material". 24th Int. Conf. on Telecommunications (ICT), Limassol, 2017, pp. 1-5.
Zhu, Jun.; Park, T.; Tinghui, Z.; Alexei, A.. Efros (2017). "Unpaired Image-to Image Translation using Cycle-Consistent Adversarial Networks" IEEE Int. Conf. on Computer Vision (ICCV).
Good, I. J. (1965). "The Estimation of Probabilities: An Essay on Modern Bayesian Methods", M.I.T. Press.
Goodfellow, I. J.; Pouget-Abadie, J.; Mirza M.; Xu, B.; Warde-Farley D.; Sherjil O.; Aaron C.; Yoshua B. (2014). "Generative Adversarial Nets." Neural Inf. Processing Systems (NIPS).
He, K.; Zhang, X.; Ren, S.; Sun, J. (2015). "Deep residual learning for image recognition". CoRR, abs/1512.03385, 2015.
Karamizadeh, S. e Arabsorkhi, A. (2018). "Methods of Pornography Detection: Review". In Proceedings of the 10th International Conference on Computer Modeling and Simulation (ICCMS 2018). ACM, New York, NY, USA, 33-38. DOI: https://doi.org/10.1145/3177457.3177484.
Krizhevsky, A.; Sutskever, I.; Hinton, G.E. (2012). "ImageNet classification with deep convolutional neural networks", In Advances in neural information processing systems, pages 1097–1105.
McClelland, D.; Marturana, F. (2014). "A Digital Forensics Triage Methodology based on Feature Manipulation Techniques", In: Proc. of the International Conference on Communications Workshops, pages. 676-681.
Macedo, J.; Costa, F.; Santos, J.A. dos. (2018). "A Benchmark Methodology for Child Pornography Detection". 31st SIBGRAPI Conf. on Graphics, Patterns and Images (SIBGRAPI).
Moreira, D.; Avila, S.; Perez, M.; Moraes, D.; Testoni, V.; Valle, E.; Goldenstein, S.; Rocha, A. (2016). "Pornography classification: The hidden clues in video space- time", Forensic Science Int. Vol. 268, p. 46-61.
Norton, A.P.; Qi, Y. (2017). "Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning". IEEE Symposium on Visualization for Cyber Security (VizSec).
Oquab, M.; Bottou, L.; Laptev, I.; Sivic, J. (2014). "Learning and transferring mid-level image representations using convolutional neural networks" in Proc. of the IEEE conf. on computer vision and pattern recognition, pp. 1717–1724.
Polastro, M.C. e Eleuterio, P.M.S. (2010). "NuDetective: a Forensic Tool to Help Combat Child Pornography through Automatic Nudity Detection", Workshop on Database and Expert Systems Applications (DEXA).
Ponti, A. M.; Ribeiro, L.S.F.; Nazare, T.S.; Bui, T.; Collomosse, J. (2017). "Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask", 30th SIBGRAPI Conf. on Graphics, Patterns and Images Tutorials (SIBGRAPI-T), Niterói, 2017, pp. 17-41.
Sae-Bae, N.; Sun, X.; Sencar, H.T.; Memon, N.D. (2014). "Towards automatic detection of child pornography", In: The 2014 IEEE Int. Conf. on Image Processing (ICIP), pages 5332–5336.
Sebastiani, F. "Machine Learning in Automated Text Categorization", ACM Computing Surveys, Vol. 34. No. 1, Março 2002, p.1-47.
Simonyan, k.; e Zisserman, A. (2014). "Very deep convolutional networks for large- scale image recognition," CoRR, vol. abs/1409.1556, 2014.
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. (2016). "Rethinking the inception architecture for computer vision". In Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, pages 2818–2826, 2016.
Ulges, A.; e Stahl, A. (2011). "Automatic detection of child pornography using color visual words," in Multimedia and Expo (ICME), 2011 IEEE Int. Conf. on. IEEE, 2011, pp. 1–6.
Vitorino, P.; Avila, S.; Rocha, A. (2016). "A Two-tier Image Representation Approach to Detecting Child Pornography". Proc. of XII Workshop de Visão Computacional.
Vitorino, P.; Avila, S.; Perez, M.; Rocha, A. (2018). "Leveraging deep neural networks to ght child pornography in the age of social media". Journal of Visual Communication and Image Representation (50).
Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J. (2016). "Practical Machine Learning Tools and Techniques", Editora Morgan Kaufmann. 4th Edition.
Yiallourou, E.; Demetriou, R.; Lanitis, A. (2017). "On the Detection of Images Containing Child-Pornographic Material". 24th Int. Conf. on Telecommunications (ICT), Limassol, 2017, pp. 1-5.
Zhu, Jun.; Park, T.; Tinghui, Z.; Alexei, A.. Efros (2017). "Unpaired Image-to Image Translation using Cycle-Consistent Adversarial Networks" IEEE Int. Conf. on Computer Vision (ICCV).
Publicado
02/09/2019
Como Citar
MALLMANN, Jackson; SANTIN, Altair; BRITTO, Alceu; SANTOS, Roger.
Mitigando os Efeitos de GAN em Classificação de Imagens com CNN. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 19. , 2019, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 281-294.
DOI: https://doi.org/10.5753/sbseg.2019.13978.
