【验证码识别】OpenCV挑战腾讯防水墙滑动验证码

使用 OpenCV破解腾讯防水墙滑动验证码

戳这里→康康你手机号在过多少网站注册过！！！
友情推荐：新一代安全短信

* 验证码地址：https://007.qq.com/online.html
* 使用OpenCv模板匹配
* 成功率90%左右
* Java + Selenium + OpenCV

产品样例

来吧！展示！

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↓↓↓

/**
	 * 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;
	}

看操作，no bb，直接上代码

private final String INDEX_URL = "https://007.qq.com/online.html?ADTAG=index.head";
private void seleniumTest() {
   
		ChromeDriverManager manager = ChromeDriverManager.getInstance();
		int status = -1;
		try {
   
			WebDriver driver = manager.getDriver();
			driver.get(INDEX_URL);
			driver.manage().window().maximize(); // 设置浏览器窗口最大化
			Thread.sleep(10000);
			driver.findElement(By.className("wp-onb-tit")).findElements(By.tagName("a")).get(1).click();
			Thread.sleep(500);
			// 点击出现滑动图
			waitWebElement(driver, By.id("code"), 500).click();
			Thread.sleep(100);
			// 获取到验证区域
			driver.switchTo().frame(waitWebElement(driver, By.id("tcaptcha_iframe"), 500));
			Thread.sleep(100);
			// 获取滑动按钮
			WebElement moveElemet = waitWebElement(driver, By.id("tcaptcha_drag_button"), 500);
			Thread.sleep(100);
			// 获取带阴影的背景图
			String bgUrl = waitWebElement(driver, By.id("slideBg"), 500).getAttribute("src");
			Thread.sleep(100);
			// 获取带阴影的小图
			String sUrl = waitWebElement(driver, By.id("slideBlock"), 500).getAttribute("src");
			Thread.sleep(100);
			// 获取高度
			String topStr = waitWebElement(driver, By.id("slideBlock"), 500).getAttribute("style").substring(32, 36);
			int top = Integer.parseInt(topStr.substring(0, topStr.indexOf("p"))) * 2;
			Thread.sleep(100);
			// 计算移动距离
			int distance = (int) Double.parseDouble(getTencentDistance(bgUrl, sUrl, top));
			// 滑动
			move(driver, moveElemet, distance);
			Thread.sleep(5000);

		} catch (Exception e) {
   
			e.printStackTrace();
		} finally {
   
			manager.closeDriver(status);
		}
	}

	/**
	 * 获取腾讯验证滑动距离
	 * 
	 * @return
	 */
	public static String dllPath = "C://chrome//opencv_java440.dll";

	public String getTencentDistance(String bUrl, String sUrl, int top) {
   
		System.load(dllPath);
		File bFile = new File("C:/qq_b.jpg");
		File sFile = new File("C:/qq_s.jpg");
		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
			bgBI = bgBI.getSubimage(360, top, bgBI.getWidth() - 370, sBI.getHeight());
			ImageIO.write(bgBI, "png", bFile);
			Mat s_mat = Imgcodecs.imread(sFile.getPath());
			Mat b_mat = Imgcodecs.imread(bFile.getPath());
			// 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
			// 归一化相关匹配法
			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, 0, 0, 0));
			return "" + ((matchLocation.x + s_mat.cols() + 360 - sBI.getWidth() - 46) / 2);
		} catch (Throwable e) {
   
			e.printStackTrace();
			return null;
		} finally {
   
			bFile.delete();
			sFile.delete();
		}
	}
/**
	 * 
	 * @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;
	}

谷歌已经宣布退出图形验证码服务，为何国内各种奇葩验证方式层出不穷，安全性到底如何？
《腾讯防水墙滑动拼图验证码》
《百度旋转图片验证码》
《网易易盾滑动拼图验证码》
《顶象区域面积点选验证码》
《顶象滑动拼图验证码》
《极验滑动拼图验证码》
《使用深度学习来破解 captcha 验证码》

戳这里→康康你手机号在过多少网站注册过！！！
友情推荐：新一代安全短信