Methods of Pornography Detection: Review

In recent years, prone images and other such indecent matter are available on the social media and the Internet for children. Filtering of image porn has become one of the big changes for searches; they are tied to finding methods to filter porn images. Social media network is interested in filter porn images from normal ones. Analysis method uses the bright image to automatically detect and filter images in the media. In this paper, we have reviewed methods such as color based, shape based, local and global feature approach, deep learning and bag-of-words for filtering porn images which include comparing with the advantages and disadvantages.

[1]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[2]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[4]  Dan Oneata,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[5]  Wen Gao,et al.  Shape-based Adult Image Detection , 2006, Int. J. Image Graph..

[6]  Luc Van Gool,et al.  An adaptive color-based particle filter , 2003, Image Vis. Comput..

[7]  Andrew Blake,et al.  Contour-based learning for object detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[8]  Marc'Aurelio Ranzato,et al.  Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.

[9]  B. Eswara Reddy,et al.  A Key Point Selection Shape Technique for Content Based Image Retrieval System , 2016, Int. J. Comput. Vis. Image Process..

[10]  Pascal Vincent,et al.  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.

[11]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[12]  Aníbal R. Figueiras-Vidal,et al.  Does diversity improve deep learning? , 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

[13]  James M. Rehg,et al.  Statistical Color Models with Application to Skin Detection , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[14]  Mitisha Narottambhai Patel,et al.  A Survey on Feature Extraction Techniques for Shape based Object Recognition , 2016 .

[15]  Youbao Tang,et al.  Offline Text-Independent Writer Identification Based on Scale Invariant Feature Transform , 2014, IEEE Transactions on Information Forensics and Security.

[16]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[17]  Graham W. Taylor,et al.  Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Thomas S. Huang,et al.  Image Classification Using Super-Vector Coding of Local Image Descriptors , 2010, ECCV.

[19]  Thomas Hofmann,et al.  Unsupervised Learning by Probabilistic Latent Semantic Analysis , 2004, Machine Learning.

[20]  Liang-Tien Chia,et al.  Local features are not lonely – Laplacian sparse coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[21]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[22]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[23]  Nasir D. Memon,et al.  Towards automatic detection of child pornography , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[24]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[25]  Wen Gao,et al.  Adult Image Detection Method Base-on Skin Color Model and Support Vector Machine , 2001 .

[26]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[27]  Yihong Gong,et al.  Nonlinear Learning using Local Coordinate Coding , 2009, NIPS.

[28]  Jing-Yu Yang,et al.  Content-based image retrieval using color difference histogram , 2013, Pattern Recognit..

[29]  Tony Lindeberg,et al.  Scale Invariant Feature Transform , 2012, Scholarpedia.

[30]  Changzhen Hu,et al.  A Breast Detecting Algorithm for Adult Image Recognition , 2009, 2009 International Conference on Information Management, Innovation Management and Industrial Engineering.

[31]  Honglak Lee,et al.  Sparse deep belief net model for visual area V2 , 2007, NIPS.

[32]  Mazdak Zamani,et al.  Security Features Comparison of Master Key and IKM Cryptographic Key Management for Researchers and Developers , 2011 .

[33]  Saeid Nahavandi,et al.  Supervised learning probabilistic Latent Semantic Analysis for human motion analysis , 2013, Neurocomputing.

[34]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[35]  Tat-Seng Chua,et al.  An integrated color-spatial approach to content-based image retrieval , 1995, MULTIMEDIA '95.

[36]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

[37]  Rudolf Hauke,et al.  Filtering adult image content with topic models , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[38]  Jiquan Ngiam,et al.  Learning Deep Energy Models , 2011, ICML.

[39]  Ramin Zabih,et al.  Comparing images using joint histograms , 1999, Multimedia Systems.

[40]  Adams Wai-Kin Kong,et al.  Using a CNN ensemble for detecting pornographic and upskirt images , 2016, 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS).

[41]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[42]  Yoshua Bengio,et al.  Deep Learning of Representations for Unsupervised and Transfer Learning , 2011, ICML Unsupervised and Transfer Learning.

[43]  Wei Wei,et al.  Color pornographic image detection based on color-saliency preserved mixture deformable part model , 2017, Multimedia Tools and Applications.

[44]  Amin Sedaghat,et al.  Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Saman Shojae Chaeikar,et al.  Comparative Analysis of Master-Key and Interpretative Key Management (IKM) Frameworks , 2012 .

[46]  Michel Devy,et al.  Global and Local Image Descriptors for Content Based Image Retrieval and Object Recognition , 2011 .

[47]  Geoffrey E. Hinton,et al.  Deep Boltzmann Machines , 2009, AISTATS.

[48]  Akram M. Zeki,et al.  PSW statistical LSB image steganalysis , 2016, Multimedia Tools and Applications.

[49]  Michael S. Lew,et al.  Deep learning for visual understanding: A review , 2016, Neurocomputing.

[50]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[51]  Chih-Fong Tsai,et al.  Bag-of-Words Representation in Image Annotation: A Review , 2012 .

[52]  Wilhelm Burger,et al.  Scale-Invariant Feature Transform (SIFT) , 2016 .

[53]  P. Peer,et al.  Human skin color clustering for face detection , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[54]  Cheng-Yuan Liou,et al.  Autoencoder for words , 2014, Neurocomputing.

[55]  C. Jeong,et al.  Appearance-based nude image detection , 2004, ICPR 2004.

[56]  Andrew Zisserman,et al.  Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.