TensorFlow学习之深度学习破解验证码

心已赠人 2022-06-07 03:57 245阅读 0赞

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在同学的演讲中了解到了TensorFlow，上网查TensorFlow最初是由Google大脑小组的工程师开发出来的，用于的是机器学习和深度神经网络方面的研究。现在已经完全开源，可以拿这个用于语音识别和图像识别等机器学习。

于是找了个例子练习下，以前做验证码破解都是直接使用别人的库，效率低识别差，好的需要自己写算法或者是购买第三方的，比较麻烦。使用了这个机器学习的验证码识别，感觉这个cnn的识别效果还是不错的。下面具体来看下怎么实现深度学习验证码破解。

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## 前言 ##

验证码主要是用于防刷，传统的验证码算法是通过把验证码分割为单个的字符，然后逐个的识别，如果复杂的验证码字母之间相互重叠，那么就无法识别了，这里采用的cnn可以对验证码整体进行有效的识别。  
学习的要点：

1、captcha库生成验证码  
2、如何将验证码识别问题转化为分类问题  
3、可以训练自己的验证码识别模型

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## 生成验证码训练数据 ##

所有的训练模型，数据才是关键，这里采用的captcha库生成验证码，captcha可以生成语音和图片验证码，验证码是由数字、大小写字母（也可以自定义特殊字符），长度为4，共有62^4种组合。

本文的系统为centos，首先安装captcha库：

sudo pip install captcha

**验证码生成器：**

采用python中生成器方式来生成训练数据，这样的好处是不需要提前生成大量的数据，而是在训练的过程中生成数据，可以无限的生成数据。

创建一个源文件：

vim generate_captcha.py

参考代码：

#!/usr/bin/python
    # -*- coding: utf-8 -*
    from captcha.image import ImageCaptcha
    from PIL import Image
    import numpy as np
    import random
    import string
    class generateCaptcha():
        def __init__(self, width = 160,#验证码图片的宽 height = 60,#验证码图片的高 char_num = 4,#验证码字符个数 characters = string.digits + string.ascii_uppercase + string.ascii_lowercase):#验证码组成，数字+大写字母+小写字母
            self.width = width
            self.height = height
            self.char_num = char_num
            self.characters = characters
            self.classes = len(characters)
        def gen_captcha(self,batch_size = 50):
            X = np.zeros([batch_size,self.height,self.width,1])
            img = np.zeros((self.height,self.width),dtype=np.uint8)
            Y = np.zeros([batch_size,self.char_num,self.classes])
            image = ImageCaptcha(width = self.width,height = self.height)
            while True:
                for i in range(batch_size):
                    captcha_str = ''.join(random.sample(self.characters,self.char_num))
                    img = image.generate_image(captcha_str).convert('L')
                    img = np.array(img.getdata())
                    X[i] = np.reshape(img,[self.height,self.width,1])/255.0
                    for j,ch in enumerate(captcha_str):
                        Y[i,j,self.characters.find(ch)] = 1
                Y = np.reshape(Y,(batch_size,self.char_num*self.classes))
                yield X,Y
        def decode_captcha(self,y):
            y = np.reshape(y,(len(y),self.char_num,self.classes))
            return ''.join(self.characters[x] for x in np.argmax(y,axis = 2)[0,:])
        def get_parameter(self):
            return self.width,self.height,self.char_num,self.characters,self.classes
        def gen_test_captcha(self):
            image = ImageCaptcha(width = self.width,height = self.height)
            captcha_str = ''.join(random.sample(self.characters,self.char_num))
            img = image.generate_image(captcha_str)
            img.save(captcha_str + '.jpg')

然后执行源文件：

python
    >>>import generate_captcha
    >>>g = generate_captcha.generateCaptcha()
    >>>g.gen_test_captcha()

然后可以在当前目录下查看生成的验证码

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## 验证码识别模型 ##

将验证码识别问题转化为分类问题，总共62^4中类型，采用4个one-hot编码分别表示4中字符取值

**cnn验证码识别模型**

3 层隐藏层、2 层全连接层，对每层都进行 dropout。  
input——>conv——>pool——>dropout——>conv——>pool——>dropout——>conv——>pool——>dropout——>fully connected layer——>dropout——>fully connected layer——>output

创建源文件captcha\_model.py，参考代码如下：

#!/usr/bin/python
    # -*- coding: utf-8 -*
    import tensorflow as tf
    import math
    class captchaModel():
        def __init__(self, width = 160, height = 60, char_num = 4, classes = 62):
            self.width = width
            self.height = height
            self.char_num = char_num
            self.classes = classes
        def conv2d(self,x, W):
            return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
        def max_pool_2x2(self,x):
            return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                                  strides=[1, 2, 2, 1], padding='SAME')
        def weight_variable(self,shape):
            initial = tf.truncated_normal(shape, stddev=0.1)
            return tf.Variable(initial)
        def bias_variable(self,shape):
            initial = tf.constant(0.1, shape=shape)
            return tf.Variable(initial)
        def create_model(self,x_images,keep_prob):
            #first layer
            w_conv1 = self.weight_variable([5, 5, 1, 32])
            b_conv1 = self.bias_variable([32])
            h_conv1 = tf.nn.relu(tf.nn.bias_add(self.conv2d(x_images, w_conv1), b_conv1))
            h_pool1 = self.max_pool_2x2(h_conv1)
            h_dropout1 = tf.nn.dropout(h_pool1,keep_prob)
            conv_width = math.ceil(self.width/2)
            conv_height = math.ceil(self.height/2)
            #second layer
            w_conv2 = self.weight_variable([5, 5, 32, 64])
            b_conv2 = self.bias_variable([64])
            h_conv2 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout1, w_conv2), b_conv2))
            h_pool2 = self.max_pool_2x2(h_conv2)
            h_dropout2 = tf.nn.dropout(h_pool2,keep_prob)
            conv_width = math.ceil(conv_width/2)
            conv_height = math.ceil(conv_height/2)
            #third layer
            w_conv3 = self.weight_variable([5, 5, 64, 64])
            b_conv3 = self.bias_variable([64])
            h_conv3 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout2, w_conv3), b_conv3))
            h_pool3 = self.max_pool_2x2(h_conv3)
            h_dropout3 = tf.nn.dropout(h_pool3,keep_prob)
            conv_width = math.ceil(conv_width/2)
            conv_height = math.ceil(conv_height/2)
            #first fully layer
            conv_width = int(conv_width)
            conv_height = int(conv_height)
            w_fc1 = self.weight_variable([64*conv_width*conv_height,1024])
            b_fc1 = self.bias_variable([1024])
            h_dropout3_flat = tf.reshape(h_dropout3,[-1,64*conv_width*conv_height])
            h_fc1 = tf.nn.relu(tf.nn.bias_add(tf.matmul(h_dropout3_flat, w_fc1), b_fc1))
            h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
            #second fully layer
            w_fc2 = self.weight_variable([1024,self.char_num*self.classes])
            b_fc2 = self.bias_variable([self.char_num*self.classes])
            y_conv = tf.add(tf.matmul(h_fc1_drop, w_fc2), b_fc2)
            return y_conv

训练cnn验证码识别模型：

这里采用了64个训练样本，每100次循环采用100个测试样本检查识别准确度，当准确度大于99%时，训练结束。

这里测试的话，可以将代码中的 if acc > 0.99 的准确度降低来减少训练的时间。

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下面是一个已经训练好的训练集，可以下载下来看看最终的验证效果：

首先通过命令下载到本地：

wget http://tensorflow-1253902462.cosgz.myqcloud.com/captcha/capcha_model.zip
    unzip capcha_model.zip

在当前目录下创建文件train\_captcha.py，内容参考下面代码：

#!/usr/bin/python
    import tensorflow as tf
    import numpy as np
    import string
    import generate_captcha
    import captcha_model
    if __name__ == '__main__':
        captcha = generate_captcha.generateCaptcha()
        width,height,char_num,characters,classes = captcha.get_parameter()
        x = tf.placeholder(tf.float32, [None, height,width,1])
        y_ = tf.placeholder(tf.float32, [None, char_num*classes])
        keep_prob = tf.placeholder(tf.float32)
        model = captcha_model.captchaModel(width,height,char_num,classes)
        y_conv = model.create_model(x,keep_prob)
        cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_,logits=y_conv))
        train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
        predict = tf.reshape(y_conv, [-1,char_num, classes])
        real = tf.reshape(y_,[-1,char_num, classes])
        correct_prediction = tf.equal(tf.argmax(predict,2), tf.argmax(real,2))
        correct_prediction = tf.cast(correct_prediction, tf.float32)
        accuracy = tf.reduce_mean(correct_prediction)
        saver = tf.train.Saver()
        with tf.Session() as sess:
            sess.run(tf.global_variables_initializer())
            step = 0
            while True:
                batch_x,batch_y = next(captcha.gen_captcha(64))
                _,loss = sess.run([train_step,cross_entropy],feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.75})
                print ('step:%d,loss:%f' % (step,loss))
                if step % 100 == 0:
                    batch_x_test,batch_y_test = next(captcha.gen_captcha(100))
                    acc = sess.run(accuracy, feed_dict={x: batch_x_test, y_: batch_y_test, keep_prob: 1.})
                    print ('###############################################step:%d,accuracy:%f' % (step,acc))
                    if acc > 0.99:
                        saver.save(sess,"capcha_model.ckpt")
                        break
                step += 1

测试识别模型：

创建源文件predict\_captcha.py，参考代码如下：

#!/usr/bin/python
    from PIL import Image, ImageFilter
    import tensorflow as tf
    import numpy as np
    import string
    import sys
    import generate_captcha
    import captcha_model
    if __name__ == '__main__':
        captcha = generate_captcha.generateCaptcha()
        width,height,char_num,characters,classes = captcha.get_parameter()
        gray_image = Image.open(sys.argv[1]).convert('L')
        img = np.array(gray_image.getdata())
        test_x = np.reshape(img,[height,width,1])/255.0
        x = tf.placeholder(tf.float32, [None, height,width,1])
        keep_prob = tf.placeholder(tf.float32)
        model = captcha_model.captchaModel(width,height,char_num,classes)
        y_conv = model.create_model(x,keep_prob)
        predict = tf.argmax(tf.reshape(y_conv, [-1,char_num, classes]),2)
        init_op = tf.global_variables_initializer()
        saver = tf.train.Saver()
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
        with tf.Session(config=tf.ConfigProto(log_device_placement=False,gpu_options=gpu_options)) as sess:
            sess.run(init_op)
            saver.restore(sess, "capcha_model.ckpt")
            pre_list =  sess.run(predict,feed_dict={x: [test_x], keep_prob: 1})
            for i in pre_list:
                s = ''
                for j in i:
                    s += characters[j]
                print s

然后执行源文件，`python predict_captcha.py Kz2J.jpg`

执行结果：Kz2J

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在训练时间足够长的情况下，你可以采用验证码生成器生成测试数据，cnn 训练出来的验证码识别模型还是很强大的，大小写的 z 都可以区分，甚至有时候人都无法区分，该模型也可以正确的识别。这个验证码识别还是可以破解大部分简单的验证码的，但是如果碰到中文的，那就需要一个集群来进行训练了。机器学习是非常强大的，可以说是大数据和人工智能的桥接处，以后多多了解。

> 腾讯云的TensorFlowAPI体验实验：[https://cloud.tencent.com/developer/labs/lab/10187][https_cloud.tencent.com_developer_labs_lab_10187]  
> TensorFlow中文社区文档：[http://www.tensorfly.cn/tfdoc/get\_started/introduction.html][http_www.tensorfly.cn_tfdoc_get_started_introduction.html]

[https_cloud.tencent.com_developer_labs_lab_10187]: https://cloud.tencent.com/developer/labs/lab/10187
[http_www.tensorfly.cn_tfdoc_get_started_introduction.html]: http://www.tensorfly.cn/tfdoc/get_started/introduction.html