Merge pull request #24 from urdego/feat(#23)

Feat(#23): 컨텐츠 유해성 감지

build.gradle

@@ -68,6 +68,10 @@ dependencies {
 
 	// Swagger
 	implementation 'org.springdoc:springdoc-openapi-starter-webmvc-ui:2.1.0'
+
+	// Tensorflow (컨텐츠 유해성 감지)
+	implementation 'org.tensorflow:tensorflow-core-api:1.0.0-rc.2'
+	runtimeOnly 'org.tensorflow:tensorflow-core-native:1.0.0-rc.2:macosx-arm64'
 }
 
 

src/main/java/io/urdego/urdego_content_service/common/exception/ExceptionMessage.java

@@ -14,9 +14,11 @@ public enum ExceptionMessage {
     // Content save
     INVALID_FILE_FORMAT("잘못된 형식의 컨텐츠입니다."),
     CONTENT_SAVE_FAILED("컨텐츠 저장에 실패했습니다."),
-    CONTENT_MULTI_SAVE_FAILED("컨텐츠 다중 저장에 실패했습니다."),
+    CONTENT_MULTI_SAVE_FAILED("컨텐츠 다중 저장에 실패했습니다. (컨텐츠 유해성 감지)"),
     CONTENT_DELETE_FAILED("컨텐츠 삭제에 실패했습니다."),
     DIRECTORY_CREATION_FAILED("상위 폴더 생성 중 예외가 발생했습니다."),
+    CONTENT_NOT_ALLOWED("컨텐츠에 유해성이 감지되어 저장에 실패했습니다."),
+    CONTENT_ANALYSIS_FAILED("컨텐츠 분석에 실패했습니다."),
 
     // Image
     IMAGE_METADATA_FAILED("이미지 메타데이터 추출에 실패했습니다.");

src/main/java/io/urdego/urdego_content_service/domain/service/ContentCommander.java

@@ -3,6 +3,7 @@
 import io.urdego.urdego_content_service.common.exception.ExceptionMessage;
 import io.urdego.urdego_content_service.common.exception.content.UserContentException;
 import io.urdego.urdego_content_service.domain.service.dto.FileInfo;
+import io.urdego.urdego_content_service.domain.service.model.nsfw.NSFWDetector;
 import org.springframework.util.StreamUtils;
 import org.springframework.web.multipart.MultipartFile;
 
@@ -21,24 +22,32 @@ public class ContentCommander {
 
     // 컨텐츠 저장
     public static FileInfo saveContent(Long userId, MultipartFile content) {
-        String filename = createFilename(userId, content.getOriginalFilename());
 
-        Path savedContentPath = BASE_PATH.resolve(Path.of(String.valueOf(userId), filename));
-        createParentDirectories(savedContentPath);
+        try {
+            // NSFW 검사
+            if (NSFWDetector.isNSFW(content.getBytes())) {
+                throw new UserContentException(ExceptionMessage.CONTENT_NOT_ALLOWED);
+            }
 
-        try (InputStream is = content.getInputStream();
-             OutputStream os = Files.newOutputStream(savedContentPath)) {
+            String filename = createFilename(userId, content.getOriginalFilename());
+            Path savedContentPath = BASE_PATH.resolve(Path.of(String.valueOf(userId), filename));
+            createParentDirectories(savedContentPath);
+
+            try (InputStream is = content.getInputStream();
+                 OutputStream os = Files.newOutputStream(savedContentPath)) {
+
+                // 컨텐츠 저장
+                StreamUtils.copy(is, os);
+            }
+
+            return FileInfo.builder()
+                    .fileName(filename)
+                    .savedPath(BASE_URL + "/" + userId + "/" + filename)
+                    .build();
 
-            // 컨텐츠 저장
-            StreamUtils.copy(is, os);
         } catch (IOException e) {
             throw new UserContentException(ExceptionMessage.CONTENT_SAVE_FAILED);
         }
-
-        return FileInfo.builder()
-                .fileName(filename)
-                .savedPath(BASE_URL + "/" + userId + "/" + filename)
-                .build();
     }
 
     // 컨텐츠 삭제

src/main/java/io/urdego/urdego_content_service/domain/service/model/nsfw/NSFWDetector.java

@@ -0,0 +1,93 @@
+package io.urdego.urdego_content_service.domain.service.model.nsfw;
+
+import org.tensorflow.Graph;
+import org.tensorflow.Session;
+import org.tensorflow.Tensor;
+import org.tensorflow.proto.GraphDef;
+import org.tensorflow.types.TFloat32;
+
+import javax.imageio.ImageIO;
+import java.awt.*;
+import java.awt.image.BufferedImage;
+import java.io.ByteArrayInputStream;
+import java.io.IOException;
+import java.nio.file.Files;
+import java.nio.file.Path;
+import java.nio.file.Paths;
+
+// 유해 컨텐츠 감지 (NSFW = Not Safe For Work)
+public class NSFWDetector {
+
+    private static final Path MODEL_PATH = Paths.get("src/main/resources/nsfw.pb");
+    private static final String INPUT_TENSOR = "input_1";
+    private static final String OUTPUT_TENSOR = "dense_3/Softmax";
+    private static final double NSFW_RATIO = 0.6;
+    private static final Graph graph;
+
+    static {
+        try {
+            graph = new Graph();
+            byte[] graphDef = Files.readAllBytes(MODEL_PATH);
+            graph.importGraphDef(GraphDef.parseFrom(graphDef));
+        } catch (IOException e) {
+            throw new RuntimeException("Failed to load GraphDef", e);
+        }
+    }
+
+
+    public static boolean isNSFW(byte[] imageBytes) throws IOException {
+        try (Session session = new Session(graph);
+             Tensor inputTensor = preprocessImage(imageBytes)) {
+
+            try (Tensor outputTensor = session.runner()
+                    .feed(INPUT_TENSOR, inputTensor) // 입력 텐서 이름
+                    .fetch(OUTPUT_TENSOR)    // 출력 텐서 이름
+                    .run()
+                    .get(0)) {
+
+
+                try (var rawTensor = outputTensor.asRawTensor()) {
+                    // float 데이터 버퍼 가져오기
+                    var floatBuffer = rawTensor.data().asFloats();
+                    int outputSize = (int) outputTensor.shape().asArray()[1]; // 출력 클래스 개수 확인
+                    float[] probabilities = new float[outputSize];
+
+                    // float 배열로 복사
+                    floatBuffer.read(probabilities);
+
+                    // 인덱스 3과 4의 값 확인
+                    float nsfwProbabilityClass3 = probabilities[3]; // NSFW 3 (Explicit NSFW Content)
+                    float nsfwProbabilityClass4 = probabilities[4]; // NSFW 4 (Porno NSFW Content)
+
+                    // NSFW 여부 판단
+                    return nsfwProbabilityClass3 > NSFW_RATIO || nsfwProbabilityClass4 > NSFW_RATIO;
+                }
+            }
+        }
+    }
+
+    private static TFloat32 preprocessImage(byte[] imageBytes) throws IOException {
+        BufferedImage img = ImageIO.read(new ByteArrayInputStream(imageBytes));
+
+        // 224x224로 리사이즈
+        BufferedImage resized = new BufferedImage(224, 224, BufferedImage.TYPE_INT_RGB);
+        Graphics2D g = resized.createGraphics();
+        g.drawImage(img, 0, 0, 224, 224, null);
+        g.dispose();
+
+        // [0, 1] 범위로 정규화된 float 배열 생성
+        float[][][][] inputData = new float[1][224][224][3];
+        for (int y = 0; y < 224; y++) {
+            for (int x = 0; x < 224; x++) {
+                int rgb = resized.getRGB(x, y);
+                inputData[0][y][x][0] = ((rgb >> 16) & 0xFF) / 255.0f; // Red
+                inputData[0][y][x][1] = ((rgb >> 8) & 0xFF) / 255.0f;  // Green
+                inputData[0][y][x][2] = (rgb & 0xFF) / 255.0f;         // Blue
+            }
+        }
+
+        // Tensor 생성
+        return TFloat32.tensorOf(org.tensorflow.ndarray.StdArrays.ndCopyOf(inputData));
+    }
+
+}