基於深度學習破解網站驗證碼106/01/17

陳柏皓 N96041119黃偉鑫 N96041101

Outline

Motivation Related Work Methodology Demo Extension

Motivation When using the internet, we found

that sometimes we need to type a few words to prove we are not machines. Therefore, we are wondering about this system is reliable or not.

Related Work Try to use Convolution Neural

Network(CNN) to classification image End-to-end or segmentation

CAPTCHA Recognition with Active Deep Learning

The End is Nigh: Generic Solving of Text-based CAPTCHAs

Related Work For each Captcha format choose the best

solution

Methodology End to end classification

Large training dataset Multi-label problem

Generate training dataset Use PHP plugin

Define Label Per char 62 dims Images label : 62*4=248 dims One hot encoding

Methodology Training Network Design

Layer 1: Convolution (32,3,3) , Activation: relu

Layer 2: MaxPooling (2,2) , Dropout: 0.25 Layer 3: Convolution (64,3,3) , Activation:

relu Layer 4: MaxPooling (2,2) , Dropout: 0.25 Layer 5: Convolution (128,3,3) , Activation:

relu Layer 6: MaxPooling (2,2) , Dropout: 0.25 Layer 7: Dense(2048), Activation: relu,

Dropout: 0.5 Layer 8: Dense(248), Activation: softmax,

Dropout: 0.5 SGD(lr=1e-5, decay=0, momentum=0.9)

Methodology Training:

Epoch: 400 Image size : 22*60 * 3 * 10000

Qualitative Evaluation Error rate: 5%

Demo Result:

Extension For Sequence multi-label problem ,with

RNN can get better performance. Improve Optical Character

Recognition(OCR) accuracy. Help blinds or Visually Impaired people

read letter or other handwritten notes.