使用java + selenium + OpenCV破解网易易盾滑动验证码的示例

红太狼 2022-10-27 05:24 507阅读 0赞

网易易盾：dun.163.com

1  
2  
3  
\* 验证码地址：https://dun.163.com/trial/jigsaw  
\* 使用OpenCv模板匹配  
\* Java + Selenium + OpenCV  
产品样例

在这里插入图片描述  
在这里插入图片描述  
接下来就是见证奇迹的时刻！

在这里插入图片描述

在这里插入图片描述  
注意！！！  
· 在模拟滑动时不能按照相同速度或者过快的速度滑动，需要向人滑动时一样先快后慢，这样才不容易被识别。  
模拟滑动代码↓↓↓

1  
2  
3  
4  
5  
6  
7  
8  
9  
10  
11  
12  
13  
14  
15  
16  
17  
18  
19  
20  
21  
22  
23  
24  
25  
26  
27  
28  
29  
30  
31  
32  
33  
34  
35  
36  
37  
38  
39  
40  
41  
42  
43  
44  
45  
46  
47  
48  
49  
50  
51  
52  
53  
54  
55  
56  
57  
58  
/\*\*  
\* 模拟人工移动  
\* @param driver  
\* @param element页面滑块  
\* @param distance需要移动距离  
\*/  
public static void move(WebDriver driver, WebElement element, int distance) throws InterruptedException \{  
int randomTime = 0;  
if (distance > 90) \{  
randomTime = 250;  
\} else if (distance > 80 && distance <= 90) \{  
randomTime = 150;  
\}  
List<Integer> track = getMoveTrack(distance - 2);  
int moveY = 1;  
try \{  
Actions actions = new Actions(driver);  
actions.clickAndHold(element).perform();  
Thread.sleep(200);  
for (int i = 0; i < track.size(); i++) \{  
actions.moveByOffset(track.get(i), moveY).perform();  
Thread.sleep(new Random().nextInt(300) + randomTime);  
\}  
Thread.sleep(200);  
actions.release(element).perform();  
\} catch (Exception e) \{  
e.printStackTrace();  
\}  
\}  
/\*\*  
\* 根据距离获取滑动轨迹  
\* @param distance需要移动的距离  
\* @return  
\*/  
public static List<Integer> getMoveTrack(int distance) \{  
List<Integer> track = new ArrayList<>();// 移动轨迹  
Random random = new Random();  
int current = 0;// 已经移动的距离  
int mid = (int) distance \* 4 / 5;// 减速阈值  
int a = 0;  
int move = 0;// 每次循环移动的距离  
while (true) \{  
a = random.nextInt(10);  
if (current <= mid) \{  
move += a;// 不断加速  
\} else \{  
move -= a;  
\}  
if ((current + move) < distance) \{  
track.add(move);  
\} else \{  
track.add(distance - current);  
break;  
\}  
current += move;  
\}  
return track;  
\}

操作过程

在这里插入图片描述

1  
2  
3  
4  
5  
6  
7  
8  
9  
10  
11  
12  
13  
14  
15  
16  
17  
18  
19  
20  
21  
22  
23  
24  
25  
26  
27  
28  
29  
30  
31  
32  
33  
34  
35  
36  
37  
38  
39  
40  
41  
42  
43  
44  
45  
46  
47  
48  
49  
50  
51  
52  
53  
54  
55  
56  
57  
58  
59  
60  
61  
62  
63  
64  
65  
66  
67  
68  
69  
70  
71  
72  
73  
74  
75  
76  
77  
78  
79  
80  
81  
82  
83  
84  
85  
86  
87  
88  
89  
90  
91  
92  
93  
94  
95  
96  
97  
98  
99  
100  
101  
102  
103  
104  
105  
106  
107  
108  
109  
110  
111  
112  
113  
114  
115  
116  
117  
118  
119  
120  
121  
122  
123  
124  
125  
126  
127  
128  
129  
130  
131  
132  
133  
134  
135  
136  
137  
138  
139  
140  
141  
142  
143  
144  
145  
146  
147  
148  
149  
150  
151  
152  
153  
154  
155  
156  
157  
158  
159  
160  
161  
162  
163  
164  
165  
166  
167  
168  
169  
170  
171  
172  
173  
174  
175  
176  
177  
178  
179  
180  
181  
182  
183  
184  
185  
186  
187  
188  
189  
190  
191  
192  
193  
194  
195  
196  
197  
198  
199  
200  
201  
202  
203  
204  
205  
206  
207  
208  
209  
210  
211  
212  
213  
214  
215  
216  
217  
/\*\*  
\* 获取网易验证滑动距离  
\*  
\* @return  
\*/  
public static String dllPath = "C://chrome//opencv\_java440.dll";  
  
public double getDistance(String bUrl, String sUrl) \{  
System.load(dllPath);  
File bFile = new File("C:/EasyDun\_b.png");  
File sFile = new File("C:/EasyDun\_s.png");  
try \{  
FileUtils.copyURLToFile(new URL(bUrl), bFile);  
FileUtils.copyURLToFile(new URL(sUrl), sFile);  
BufferedImage bgBI = ImageIO.read(bFile);  
BufferedImage sBI = ImageIO.read(sFile);  
// 裁剪  
cropImage(bgBI, sBI, bFile, sFile);  
Mat s\_mat = Imgcodecs.imread(sFile.getPath());  
Mat b\_mat = Imgcodecs.imread(bFile.getPath());  
  
//阴影部分为黑底时需要转灰度和二值化，为白底时不需要  
// 转灰度图像  
Mat s\_newMat = new Mat();  
Imgproc.cvtColor(s\_mat, s\_newMat, Imgproc.COLOR\_BGR2GRAY);  
// 二值化图像  
binaryzation(s\_newMat);  
Imgcodecs.imwrite(sFile.getPath(), s\_newMat);  
  
int result\_rows = b\_mat.rows() - s\_mat.rows() + 1;  
int result\_cols = b\_mat.cols() - s\_mat.cols() + 1;  
Mat g\_result = new Mat(result\_rows, result\_cols, CvType.CV\_32FC1);  
Imgproc.matchTemplate(b\_mat, s\_mat, g\_result, Imgproc.TM\_SQDIFF); // 归一化平方差匹配法TM\_SQDIFF 相关系数匹配法TM\_CCOEFF  
  
Core.normalize(g\_result, g\_result, 0, 1, Core.NORM\_MINMAX, -1, new Mat());  
Point matchLocation = new Point();  
MinMaxLocResult mmlr = Core.minMaxLoc(g\_result);  
matchLocation = mmlr.maxLoc; // 此处使用maxLoc还是minLoc取决于使用的匹配算法  
Imgproc.rectangle(b\_mat, matchLocation, new Point(matchLocation.x + s\_mat.cols(), matchLocation.y + s\_mat.rows()), new Scalar(0, 255, 0, 0));  
Imgcodecs.imwrite(bFile.getPath(), b\_mat);  
return matchLocation.x + s\_mat.cols() - sBI.getWidth() + 12;  
\} catch (Throwable e) \{  
e.printStackTrace();  
return 0;  
\} finally \{  
bFile.delete();  
sFile.delete();  
\}  
\}  
  
/\*\*  
\* 图片亮度调整  
\*  
\* @param image  
\* @param param  
\* @throws IOException  
\*/  
public void bloding(BufferedImage image, int param) throws IOException \{  
if (image == null) \{  
return;  
\} else \{  
int rgb, R, G, B;  
for (int i = 0; i < image.getWidth(); i++) \{  
for (int j = 0; j < image.getHeight(); j++) \{  
rgb = image.getRGB(i, j);  
R = ((rgb >> 16) & 0xff) - param;  
G = ((rgb >> 8) & 0xff) - param;  
B = (rgb & 0xff) - param;  
rgb = ((clamp(255) & 0xff) << 24) | ((clamp(R) & 0xff) << 16) | ((clamp(G) & 0xff) << 8) | ((clamp(B) & 0xff));  
image.setRGB(i, j, rgb);  
  
\}  
\}  
\}  
\}  
  
// 判断a,r,g,b值，大于256返回256，小于0则返回0,0到256之间则直接返回原始值  
private int clamp(int rgb) \{  
if (rgb > 255)  
return 255;  
if (rgb < 0)  
return 0;  
return rgb;  
\}  
  
/\*\*  
\* 生成半透明小图并裁剪  
\*  
\* @param image  
\* @return  
\*/  
private void cropImage(BufferedImage bigImage, BufferedImage smallImage, File bigFile, File smallFile) \{  
int y = 0;  
int h\_ = 0;  
try \{  
// 2 生成半透明图片  
bloding(bigImage, 75);  
for (int w = 0; w < smallImage.getWidth(); w++) \{  
for (int h = smallImage.getHeight() - 2; h >= 0; h--) \{  
int rgb = smallImage.getRGB(w, h);  
int A = (rgb & 0xFF000000) >>> 24;  
if (A >= 100) \{  
rgb = (127 << 24) | (rgb & 0x00ffffff);  
smallImage.setRGB(w, h, rgb);  
\}  
\}  
\}  
for (int h = 1; h < smallImage.getHeight(); h++) \{  
for (int w = 1; w < smallImage.getWidth(); w++) \{  
int rgb = smallImage.getRGB(w, h);  
int A = (rgb & 0xFF000000) >>> 24;  
if (A > 0) \{  
if (y == 0)  
y = h;  
h\_ = h - y;  
break;  
\}  
\}  
\}  
smallImage = smallImage.getSubimage(0, y, smallImage.getWidth(), h\_);  
bigImage = bigImage.getSubimage(0, y, bigImage.getWidth(), h\_);  
ImageIO.write(bigImage, "png", bigFile);  
ImageIO.write(smallImage, "png", smallFile);  
\} catch (Throwable e) \{  
System.out.println(e.toString());  
\}  
\}  
  
/\*\*  
\*  
\* @param mat  
\* 二值化图像  
\*/  
public static void binaryzation(Mat mat) \{  
int BLACK = 0;  
int WHITE = 255;  
int ucThre = 0, ucThre\_new = 127;  
int nBack\_count, nData\_count;  
int nBack\_sum, nData\_sum;  
int nValue;  
int i, j;  
int width = mat.width(), height = mat.height();  
// 寻找最佳的阙值  
while (ucThre != ucThre\_new) \{  
nBack\_sum = nData\_sum = 0;  
nBack\_count = nData\_count = 0;  
  
for (j = 0; j < height; ++j) \{  
for (i = 0; i < width; i++) \{  
nValue = (int) mat.get(j, i)\[0\];  
  
if (nValue > ucThre\_new) \{  
nBack\_sum += nValue;  
nBack\_count++;  
\} else \{  
nData\_sum += nValue;  
nData\_count++;  
\}  
\}  
\}  
nBack\_sum = nBack\_sum / nBack\_count;  
nData\_sum = nData\_sum / nData\_count;  
ucThre = ucThre\_new;  
ucThre\_new = (nBack\_sum + nData\_sum) / 2;  
\}  
// 二值化处理  
int nBlack = 0;  
int nWhite = 0;  
for (j = 0; j < height; ++j) \{  
for (i = 0; i < width; ++i) \{  
nValue = (int) mat.get(j, i)\[0\];  
if (nValue > ucThre\_new) \{  
mat.put(j, i, WHITE);  
nWhite++;  
\} else \{  
mat.put(j, i, BLACK);  
nBlack++;  
\}  
\}  
\}  
// 确保白底黑字  
if (nBlack > nWhite) \{  
for (j = 0; j < height; ++j) \{  
for (i = 0; i < width; ++i) \{  
nValue = (int) (mat.get(j, i)\[0\]);  
if (nValue == 0) \{  
mat.put(j, i, WHITE);  
\} else \{  
mat.put(j, i, BLACK);  
\}  
\}  
\}  
\}  
\}  
// 延时加载  
private static WebElement waitWebElement(WebDriver driver, By by, int count) throws Exception \{  
WebElement webElement = null;  
boolean isWait = false;  
for (int k = 0; k < count; k++) \{  
try \{  
webElement = driver.findElement(by);  
if (isWait)  
System.out.println(" ok!");  
return webElement;  
\} catch (org.openqa.selenium.NoSuchElementException ex) \{  
isWait = true;  
if (k == 0)  
System.out.print("waitWebElement(" + by.toString() + ")");  
else  
System.out.print(".");  
Thread.sleep(50);  
\}  
\}  
if (isWait)  
System.out.println(" outTime!");  
return null;  
\}  
注意:有一个问题还没有解决，还无法区分阴影部分是黑色还是白色。 因为两种的情况不同，所以处理方式也不同。阴影部分为黑底时需要转灰度和二值化，为白底时不需要。黑底使用归一化平方差匹配算法 TM\_SQDIFF ，而白底使用相关系数匹配算法 TM\_CCOEFF。

到此这篇关于使用java + selenium + OpenCV破解网易易盾滑动验证码的文章就介绍到这了