Java实现图标点选验证码识别及分割

图标点选验证码是一种常见的防止自动化攻击的手段，用户需要点击指定的图标来完成验证。本文介绍如何使用Java结合ONNX模型和Siamese神经网络来识别和分割图标点选验证码。

一、技术背景
图标点选验证码的破解分为两部分：图标分割和相似度对比。图标分割用于检测并裁剪出验证码图片中的各个图标；相似度对比则用于确定这些图标是否与指定的目标图标相匹配。

二、图标分割
1. 图像处理
首先，加载并处理图像。为了适应模型的输入要求，需要对图像进行缩放和填充。

java

import java.awt.Graphics2D;
import java.awt.image.BufferedImage;
import javax.imageio.ImageIO;
import java.io.File;
import java.io.IOException;

public BufferedImage paddedResize(String imagePath, int newWidth, int newHeight) throws IOException {
    BufferedImage image = ImageIO.read(new File(imagePath));
    double scale = Math.min(newWidth / (double) image.getWidth(), newHeight / (double) image.getHeight());
    int scaledWidth = (int) (image.getWidth() * scale);
    int scaledHeight = (int) (image.getHeight() * scale);

    BufferedImage newImage = new BufferedImage(newWidth, newHeight, BufferedImage.TYPE_INT_RGB);
    Graphics2D g = newImage.createGraphics();
    g.setColor(java.awt.Color.WHITE);
    g.fillRect(0, 0, newWidth, newHeight);
    int x = (newWidth - scaledWidth) / 2;
    int y = (newHeight - scaledHeight) / 2;
    g.drawImage(image, x, y, scaledWidth, scaledHeight, null);
    g.dispose();
    return newImage;
}
2. ONNX模型推理
加载ONNX模型并进行目标检测。

java

import ai.onnxruntime.*;
import java.nio.FloatBuffer;

public class ObjectDetection {
    public float[][] detectObjects(BufferedImage image) throws OrtException {
        OrtEnvironment env = OrtEnvironment.getEnvironment();
        OrtSession session = env.createSession("models/icon_detection.onnx");

        // Prepare tensor input and perform inference here
        OnnxTensor inputTensor = prepareTensorInput(image, session);
        OrtSession.Result output = session.run(Collections.singletonMap("input", inputTensor));

        // Post-process the output and return bounding boxes
        float[][] boxes = processOutput(output);
        return boxes;
    }

    private OnnxTensor prepareTensorInput(BufferedImage image, OrtSession session) throws OrtException {
        // Placeholder for actual implementation
        FloatBuffer buffer = FloatBuffer.allocate(1 * 3 * 640 * 640);
        return OnnxTensor.createTensor(session.getEnvironment(), buffer, new long[]{1, 3, 640, 640});
    }

    private float[][] processOutput(OrtSession.Result output) {
        // Placeholder for actual implementation
        return new float[0][];
    }
}
3. 画框与裁剪
根据检测结果画出边框并裁剪图标。

java

import java.awt.BasicStroke;
import java.awt.Color;
import java.awt.Graphics2D;

public BufferedImage drawRectangle(String imagePath, int[] box) throws IOException {
    BufferedImage image = ImageIO.read(new File(imagePath));
    Graphics2D g = image.createGraphics();
    g.setColor(Color.RED);
    g.setStroke(new BasicStroke(3));
    g.drawRect(box[0], box[1], box[2], box[3]);
    g.dispose();
    return image;
}

public BufferedImage cropImage(String imagePath, int[] box) throws IOException {
    BufferedImage image = ImageIO.read(new File(imagePath));
    return image.getSubimage(box[0], box[1], box[2], box[3]);
}
三、相似度对比
1. Siamese神经网络
使用Siamese网络进行相似度对比。

java

import org.tensorflow.SavedModelBundle;
import org.tensorflow.Tensor;

public float getSimilarity(String modelPath, BufferedImage img1, BufferedImage img2) {
    try (SavedModelBundle model = SavedModelBundle.load(modelPath)) {
        Tensor input1 = preprocessImage(img1);
        Tensor input2 = preprocessImage(img2);
        Tensor output = model.session().runner().feed("input_1", input1).feed("input_2", input2).fetch("output").run().get(0);

        float[] outputData = new float[1];
        output.copyTo(outputData);
        return outputData[0];
    }
}

private Tensor preprocessImage(BufferedImage image) {
    // Placeholder for actual implementation
    return Tensor.create(new float[1][60][60][3]);
}
四、测试与结果
进行模型测试并展示结果。

java

public class Main {
    public static void main(String[] args) {
        try {
            String imagePath = "path/to/image.jpg";更多内容联系1436423940
            BufferedImage resizedImage = paddedResize(imagePath, 640, 640);

            ObjectDetection detector = new ObjectDetection();
            float[][] boxes = detector.detectObjects(resizedImage);

            for (int i = 0; i < boxes.length; i++) {
                int[] box = { (int) boxes[i][0], (int) boxes[i][1], (int) boxes[i][2], (int) boxes[i][3] };
                BufferedImage croppedImage = cropImage(imagePath, box);
                ImageIO.write(croppedImage, "png", new File("cropped_image_" + i + ".png"));
            }

        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}