import streamlit as st import os import time from PIL import Image # 全局缓存 Whisper 语音大模型 @st.cache_resource def load_whisper_model(): import whisper return whisper.load_model("small") # 页面配置 st.set_page_config( page_title="多模态AI生成痕迹鉴别系统", page_icon="🔍", layout="wide" ) # ========================================== # 侧边栏：系统设置与说明 # ========================================== with st.sidebar: st.title("⚙️ 鉴别引擎设置") st.markdown("提供轻量、可解释的多模态AI生成内容快速鉴别能力。") st.divider() st.header("🎚️ 动态判定阈值") st.markdown("调整敏感度，适应不同审核场景：") high_risk_threshold = st.slider( "高危报警阈值", min_value=0.60, max_value=0.95, value=0.80, step=0.05, help="高于此值判定为“高度疑似AI生成”" ) warning_threshold = st.slider( "存疑缓冲阈值", min_value=0.20, max_value=0.55, value=0.40, step=0.05, help="介于警告阈值与报警阈值之间时，标记为“存疑”" ) st.divider() st.caption("🔹 引擎状态：轻量模型已缓存 · 本地推理") # ========================================== # 主界面标题 # ========================================== st.title("🔍 多模态AI生成痕迹快速鉴别系统") st.markdown( "同时支持**图像、文本、视频**单模态扫描，以及**图文/视文**联合跨模态融合鉴定。" ) work_mode = st.radio( "请选择检测模式：", ["单模态独立检测（图片 / 文本 / 视频）", "多模态联合检测（图片+文案 / 视频+文案）"], horizontal=True ) st.divider() # ========================================== # 模式一：单模态独立检测 # ========================================== if "单模态独立检测" in work_mode: with st.container(border=True): uploaded_file = st.file_uploader( "拖拽或点击上传待检测文件", type=["jpg", "png", "jpeg", "txt", "docx", "mp4", "avi", "mov"], help="支持常见图片、文本、视频格式，单文件≤50MB，视频时长建议≤5分钟" ) if uploaded_file is not None: file_type = uploaded_file.name.split('.')[-1].lower() st.success(f"✅ 已接收：**{uploaded_file.name}** | 启动鉴别引擎...") col_content, col_result = st.columns([1, 1.2], gap="large") # ----------------- 图像检测 ----------------- if file_type in ['jpg', 'png', 'jpeg']: with col_content: st.subheader("原始图像") st.image(uploaded_file, use_container_width=True) st.subheader("AI痕迹热力图（Grad-CAM）") heatmap_placeholder = st.empty() with col_result: st.subheader("⚙️ 特征提取中...") progress_bar = st.progress(0) status_text = st.empty() for percent_complete in range(100): time.sleep(0.01) progress_bar.progress(percent_complete + 1) status_text.text(f"正在提取LBP纹理、频域伪影及深度语义... {percent_complete + 1}%") from image_module import analyze_image, load_deep_image_model, generate_image_heatmap result = analyze_image(uploaded_file) model, device = load_deep_image_model() # 图像模块内部我们已经处理过 seek(0)，所以这里可以直接传 heatmap_img = generate_image_heatmap(uploaded_file, model, device) heatmap_placeholder.image( heatmap_img, caption="🔴 红色区域为模型判定的高可疑伪造区域", use_container_width=True ) status_text.text("特征提取完成") st.subheader("检测报告") final_prob = result['final_probability'] if final_prob >= high_risk_threshold: st.error(f"高度疑似AI生成图像（生成概率：{final_prob * 100:.1f}%）") elif warning_threshold <= final_prob < high_risk_threshold: st.warning(f"可疑图像，可能经过AI修图或局部生成（概率：{final_prob * 100:.1f}%）") else: st.success(f"真实图像，未检出明显生成痕迹（概率：{final_prob * 100:.1f}%）") st.progress(float(final_prob)) with st.expander("展开底层特征参数", expanded=True): st.info( f"**双轨特征得分：**\n\n" f"- 传统物理特征异常度：{result['traditional_score'] * 100:.1f}%\n" f"- MobileNetV2 深度特征：{result['deep_score'] * 100:.1f}%" ) # ----------------- 文本检测 ----------------- elif file_type in ['txt', 'docx']: text_content = "" uploaded_file.seek(0) # 核心修复：倒带文件指针 if file_type == 'txt': text_content = uploaded_file.read().decode("utf-8") elif file_type == 'docx': import docx doc = docx.Document(uploaded_file) text_content = "\n".join([para.text for para in doc.paragraphs]) with col_content: st.subheader("原始文本内容") text_placeholder = st.empty() text_placeholder.text_area("提取的文本", text_content, height=350) with col_result: st.subheader("⚙️ 语义连贯性分析中...") with st.spinner("正在计算困惑度、句法复杂度及BERT深度特征..."): from text_module import analyze_text, get_custom_text_model, generate_text_highlight_html result = analyze_text(text_content) tokenizer, model = get_custom_text_model() highlighted_html = generate_text_highlight_html(text_content, tokenizer, model) text_placeholder.markdown("**🔥 AI痕迹逐句高亮（黄色为高风险句式）**", unsafe_allow_html=True) text_placeholder.markdown(highlighted_html, unsafe_allow_html=True) st.subheader("检测报告") final_prob = result['final_probability'] if final_prob >= high_risk_threshold: st.error(f"高度疑似大语言模型生成文本（生成概率：{final_prob * 100:.1f}%）") elif warning_threshold <= final_prob < high_risk_threshold: st.warning(f"可疑文本，可能经AI润色或拼接（概率：{final_prob * 100:.1f}%）") else: st.success(f"真实人类写作风格，低风险（概率：{final_prob * 100:.1f}%）") st.progress(float(final_prob)) with st.expander("展开底层特征参数", expanded=True): st.info( f"**多维度文本特征：**\n\n" f"- 统计学风格异常度：{result['stat_score'] * 100:.1f}%\n" f"- DistilBERT 语义鉴别得分：{result['deep_score'] * 100:.1f}%\n" f"- 补充指标：{result['details']}" ) # ----------------- 视频检测 ----------------- elif file_type in ['mp4', 'avi', 'mov']: with col_content: st.subheader("视频内容预览") st.video(uploaded_file) with col_result: st.subheader("⚙️ 时空特征提取中...") with st.spinner("正在进行关键帧抽帧、光流计算及音频频谱分析..."): temp_video_path = f"temp_uploaded_video.{file_type}" with open(temp_video_path, "wb") as f: uploaded_file.seek(0) # 核心修复：倒带文件指针 f.write(uploaded_file.read()) from video_module import analyze_video result = analyze_video(temp_video_path) if os.path.exists(temp_video_path): os.remove(temp_video_path) if "error" in result: st.error(result["error"]) else: st.subheader("检测报告") final_prob = result['avg_probability'] if final_prob >= high_risk_threshold: st.error(f"整体视频高度疑似AI生成（综合概率：{final_prob * 100:.1f}%）") elif warning_threshold <= final_prob < high_risk_threshold: st.warning(f"视频存疑，存在异常帧或拼接痕迹（概率：{final_prob * 100:.1f}%）") else: st.success(f"未发现明显时序伪造痕迹，低风险（概率：{final_prob * 100:.1f}%）") st.progress(float(final_prob)) with st.expander("展开抽帧分析详情", expanded=True): st.info( f"**视频物理特征：**\n\n" f"- 总帧数：{result['total_frames']} 帧 (帧率 {result['fps']:.1f} fps)\n" f"- 关键帧采样数：{result['sampled_frames']} 帧\n" f"- 单帧最高风险值：{result['max_probability'] * 100:.1f}%" ) # ========================================== # 模式二：多模态联合检测 # ========================================== elif "多模态联合检测" in work_mode: st.subheader("🔗 跨模态联合检测场景") st.markdown( "模拟真实审核场景：同时检测**图像/视频**与**配套文本**，通过决策级加权融合输出综合AI生成概率。" ) # 初始化session_state记忆体（用于自动提取的文本） if 'auto_text' not in st.session_state: st.session_state['auto_text'] = "" col_media, col_text = st.columns(2, gap="large") with col_media: multi_media = st.file_uploader( "🖼️ / 🎞️ 第一步：上传媒体内容（图片或短视频）", type=["jpg", "png", "jpeg", "mp4", "avi", "mov"], key="multi_media" ) file_type = "" if multi_media: file_type = multi_media.name.split('.')[-1].lower() if file_type in ['mp4', 'avi', 'mov']: st.video(multi_media) # 智能语音提取按钮 if st.button("🎙️ 自动从视频提取语音转文字", use_container_width=True): with st.spinner("正在加载Whisper模型并转录音频（若视频较长请耐心等待数分钟）..."): try: from moviepy import VideoFileClip import whisper # 1. 保存临时视频 temp_vid = "temp_for_audio.mp4" with open(temp_vid, "wb") as f: multi_media.seek(0) f.write(multi_media.read()) # 2. 读取并剥离音频 temp_audio = "temp_audio.wav" my_clip = VideoFileClip(temp_vid) # 【新增防护】检查视频到底有没有声音！ if my_clip.audio is None: st.error("❌ 提取失败：检测到该视频没有声音轨道！") else: my_clip.audio.write_audiofile(temp_audio, logger=None) my_clip.close() # 3. 语音识别 model = load_whisper_model() result = model.transcribe( temp_audio, language="zh", initial_prompt="以下是一段标准的简体中文普通话录音。", fp16=False ) st.session_state['auto_text'] = result["text"] st.success("✅ 提取成功！") time.sleep(1) # 停留1秒让用户看到成功提示 st.rerun() except Exception as e: # 【新增防护】无论出什么错，直接弹在网页上！ st.error(f"❌ 提取失败，底层报错信息：{str(e)}") st.info( "💡 提示：如果看到 'ffprobe' 或 'ffmpeg' 等字眼，请确保系统已通过 conda 安装了 ffmpeg。") finally: # 【新增防护】哪怕中途崩溃，也要把占硬盘的临时文件删掉 if os.path.exists(temp_vid): try: os.remove(temp_vid) except: pass if os.path.exists(temp_audio): try: os.remove(temp_audio) except: pass else: st.image(multi_media, use_container_width=True) with col_text: multi_txt = st.text_area( "📝 第二步：输入配套文本（或点击左侧自动提取）", value=st.session_state['auto_text'], height=200, key="multi_txt" ) # 联合检测按钮 if multi_media and multi_txt: if st.button("🔍 启动跨模态融合鉴别", type="primary", use_container_width=True): st.divider() st.subheader("📊 联合检测报告") from text_module import analyze_text with st.spinner("并行调用视觉鉴别引擎与文本鉴别引擎..."): file_type = multi_media.name.split('.')[-1].lower() p_media = 0.0 media_label = "" if file_type in ['mp4', 'avi', 'mov']: from video_module import analyze_video temp_path = f"temp_multi_video.{file_type}" with open(temp_path, "wb") as f: multi_media.seek(0) # 核心修复：倒带文件指针 f.write(multi_media.read()) res_media = analyze_video(temp_path) p_media = res_media.get('avg_probability', 0) media_label = "🎞️ 视频维度AI生成概率" if os.path.exists(temp_path): os.remove(temp_path) else: from image_module import analyze_image res_media = analyze_image(multi_media) p_media = res_media['final_probability'] media_label = "🖼️ 图像维度AI生成概率" # 文本检测 res_txt = analyze_text(multi_txt) p_txt = res_txt['final_probability'] # 融合权重（视觉60%，文本40%） joint_prob = (p_media * 0.60) + (p_txt * 0.40) # 展示三指标 col_d1, col_d2, col_d3 = st.columns(3) col_d1.metric(label=media_label, value=f"{p_media * 100:.1f}%") col_d2.metric(label="📝 文本维度AI生成概率", value=f"{p_txt * 100:.1f}%") col_d3.metric( label="🔗 多模态综合判定概率", value=f"{joint_prob * 100:.1f}%", delta="加权融合 (视觉0.6 + 文本0.4)", delta_color="off" ) st.progress(float(joint_prob)) # 综合判定 if joint_prob >= high_risk_threshold: st.error("🚨 **联合研判结论：高度疑似AI生成的多模态内容**（图像/视频与文本均呈现显著生成特征）") elif warning_threshold <= joint_prob < high_risk_threshold: st.warning("⚠️ **联合研判结论：内容存疑**，可能存在局部AI修改或跨模态不一致，建议人工复核") else: st.success("✅ **联合研判结论：内容安全**，未见明显多模态伪造痕迹")