Page 38 - CATALOG RMIC

An Automated and Dynamic Anti-Pornography
System Using Twice Multi-Agent Learning in Skin
Detector and Pornography Classifier : Based on
Real Time Camera Images for both Gender (Male
and Female)
aws.alaa@fskik.upsi.edu.my
Dr. Aos Alaa Zaidan (UPSI) Bilal Bahaa Zaidan (UM)

Dr. Mashitoh Hashim (UPSI) Dr. ModiI Lakulu (UPSI) PI2015704400

Skin Detector Stage SILVER - PECIPTA 2015

Detection Phase Training Phase An automated and dynamic computerized system
for identifying pornographic content on real time
Pre-Processing Phase camera images for both gender (Male and Female)
5 1 Two Sets of Training was designed. It works on four machine learning
10 Image Input (I) Images methods in two different stages namely skin detector
BP-Neural Network method: • Skin Images (X). stage and pornography classifier stage. A
SAN technique based on RGB • Non-Skin Images (Y). multi-agent learning is used twice. The proposed
system has produced signigicant rates of TP dan TN
&& average rates (that is, 96% and 97.33% respectively).
The implementation of this algorithm is crucial and
Bayesian method: GH significant not only in identifying pornography but
also in blocking websites that covertly promote
Neural Pre-Detecting technique based on YCbCr pornography.
Process 6 For detecting For training

N1=Fun(i,net) i=SAN-(SIA) N (i)- SAN-SAN(X&Y)
- GH
-GH(X,Y) 2 Training Process

8 7 For
9 Neuralnet=Fun(SAN(X
Skin Detection
as the Image &Y))

II=Fun(I,N1,Ps,Pns) For Bayesian
Ps=Fun(GH(X))
Pns=Fun(GH(Y))

Pornography Classifier Stage

Classification Phase Training Phase

Feature Extraction Phase Four Sets of Training

Images
11 Shape Features 3 • Porn Skin Images
BP-Neural Network method:
Belong to Xas

extract thirteen features as the (D)&Porn Skin Images

Neural Pre-Classifying 12 features vector (FV) as (M), Z=D&M.
Process && • Non-Porn Skin Images

N2=Fun(K,net2) Color Features Belong to Xas (E)&
Bayesian method: GH Non-Porn Skin Images
technique based on YCbCr as (N), W=E&N.
• Non-Porn Skin Images

14 For classifyingFor training as (E) &Skin Images for
the Non-Porn Skin
Image Classification
Fun (II,N2,Pp,Pnp) K=FV(II) -FV(Z&W) Areas as (F), G=E&F.
16 -GH(G,H) 4 • S4kin Images for the

17 Porn Skin Areas as (H).

OR Non- 13 Training Process
Porn Porn 15
For
Neuralnet2=Fun(FV(Z

&W))

For Bayesian
Pp=Fun(GH(H))
Pnp=Fun(GH(G))

The Main Structure of the Proposed Anti-Pornography Algorithm

R & D Product 30