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A Brief Survey of Porn-Detection/Porn-Removal Software

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Understand it and then Detect it

There are mainly two kinds of porn-detecting/removing software, the dominate ones in the market were built based on simple criteria such as the skin-tone colors and their spatial distributions. The latest and emerging ones are built on much more advanced technologies called image understanding technologies or cognitive image search engine, the representative software of this kind is the PNWatch developed by Yang’s Scientific Research Institute, LLC., USA. In this survey we summarized the technologies available to the market. (Tucson, Arizona, 09/12/2005)

For more information about the most accurate porno detection technology, software, open source C\C++ developers' kit, freeware and professional package for detecting dirty and adult images and pornographic and xxx-rate videos, please visit [here].

Existing Technologies

Skin-Tone Based Methods


Most of porn-detection or porn-removal software use color skin tone to detect the spatial distribution of skin area and guess the possibility of the nudity of the scanned images. One example is Snitch's SkinScan algorithm that detects adult images by assessment of skin-tone levels and distribution. The drawbacks of this kind of method are:

•  Very large false-alarm rate. Since in natural, there are many objects or surfaces are of the skin color tone and the diversity of human skin color tones must introduce very big false-alarm rate for this kind of software. As an example, let us take a look at the searching results of the “Snitch Professional”. The search results are displayed in an order sorted by SkinScan; namely, the likeliness generated by the skin color tone.
The following listed those images detected by Snitch to be the most-likely pornographic images. The real porn images are blocked in white while the false alarms are shown in whole. Observe that some of the alarms are entirely surprises. Can you imagine the connection between a bowl of food as shown in the last image in the second row and any illicit images? Well, this is a typical failure of skin-tone based porn-detecting algorithms because many things around us have skin-tone colors.



Some other surprises were shown in the following screenshot. The same reason behind the false detection of a tank in the field as shown in the last image in the first row. From the point of view of image understanding technologies, this kind of detecting method can only achieve very low automation because a human operator much be in present to do the final judgments between innocent and suspect images.



•  Blind to pornography in black and white.. Who told us that pornographies are all in color? In face, many of them are in black and white (or in gray-scale if you prefer jargons from digital image processing). Obviously, all skin-tone based porn-detecting software packages fail to detect in such cases. However, the image-understanding based software such as PNWatch can still function well.
A third party review of another typical flesh tone based porno image detection software can be found at [here]. Just in case you can not visit it, the cached version is at [here](cached on 04/19/2006).

The following screenshots are taken from PixAlert Auditor 3.1. Note that this software is also most likely based on skin-tone methods. Observe from the detected results that many innocent images are misclassified only because they had many skin color regions.



Look at the rifle in the first image in the second row, what a surprise! Yes, this kind of software just doesn’t understand the image it is looking at.



None Skin-Tone Based Methods


Skeptic's image composition analysis (ICA) is a method based solely on the pattern recognition ability of neural network to detect pornographic images. The features it uses are shapes, textures, and etc. It was report that this technology can distinguish pose, facial expression, clothing and position of bodies. Still, as all the artificial neural network-based methods, the trained artificial neural networks will never reveal the inner knowledge structures of the images to the users. Therefore, large false alarm rate should be experienced.

Image Understanding Based Methods


Concerning porn-detection tasks, the difference between a machine and a person is that the person knows that only some areas of skin-color tone don’t necessary mean a pornographic scene. Many other attributes such as exposure breasts and penises contributing much more to pornographic scenes. A people understand what kind of attributes to look for in pornographic scenes. This is called “image understanding” in the jargons of digital image processing. Well, on the other hand, since “image understanding” is ill-defined in the principle of digital image processing, the technology behind image-understanding based porn-detection is not yet another “simple” image understanding methods that spread around us for a long time, it is called Physical Linguistic Vision Technologies. The software PNWatch uses much more advanced image understanding technologies such as breast-detectors and penis-detectors to feature the essential elements that constitute a pornographic image. For more information on how PNWatch works, click here.

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