cdcd69256b
- 4维指纹采集: 性能/语言/能力/行为 - models.py 已加入 IdentityFingerprintModel (第5维数据模型) - comparator.py 已升级为5维评分 (含identity维度比较) - reporter.py 已加入身份验证报告输出 - main.py 已集成identity采集流程 - identity collector 待下次提交补充完整代码
392 lines
14 KiB
Python
392 lines
14 KiB
Python
"""Report generator — Rich terminal tables + JSON export."""
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import json
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from datetime import datetime
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from pathlib import Path
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from rich.console import Console
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from rich.table import Table
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from rich.panel import Panel
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from rich.text import Text
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from rich import box
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from core.models import ComparisonResult, FullFingerprint
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console = Console()
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def print_report(result: ComparisonResult,
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genuine_fp: FullFingerprint,
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suspect_fp: FullFingerprint) -> None:
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"""Print a beautiful terminal report using Rich."""
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console.print()
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console.print(Panel(
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"[bold cyan]AI API 指纹检测对比报告[/bold cyan]\n"
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"[dim]Fingerprint Comparison Report[/dim]",
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box=box.DOUBLE,
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expand=False,
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padding=(1, 4),
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))
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console.print()
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# Channel info
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info_table = Table(box=box.SIMPLE_HEAVY, show_header=True,
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title="📡 渠道信息 / Channel Info")
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info_table.add_column("", style="bold")
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info_table.add_column("基准渠道 (Genuine)", style="green")
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info_table.add_column("待检测渠道 (Suspect)", style="yellow")
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info_table.add_row("Name", genuine_fp.channel_name, suspect_fp.channel_name)
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info_table.add_row("Timestamp", genuine_fp.timestamp, suspect_fp.timestamp)
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console.print(info_table)
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console.print()
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# Performance summary
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_print_performance_summary(genuine_fp, suspect_fp)
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console.print()
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# Identity verification summary
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_print_identity_summary(genuine_fp, suspect_fp)
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console.print()
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# Dimension scores table
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score_table = Table(
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box=box.ROUNDED,
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show_header=True,
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title="📊 维度评分 / Dimension Scores",
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title_style="bold",
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)
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score_table.add_column("维度 Dimension", style="bold cyan", min_width=14)
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score_table.add_column("得分 Score", justify="center", min_width=10)
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score_table.add_column("权重 Weight", justify="center", min_width=10)
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score_table.add_column("加权分 Weighted", justify="center", min_width=10)
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score_table.add_column("详情 Details", min_width=30)
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for ds in result.dimension_scores:
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score_val = ds.score
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if score_val >= 0.80:
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score_str = f"[green]{score_val:.3f}[/green]"
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elif score_val >= 0.60:
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score_str = f"[yellow]{score_val:.3f}[/yellow]"
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else:
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score_str = f"[red]{score_val:.3f}[/red]"
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weighted = ds.score * ds.weight
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weighted_str = f"{weighted:.3f}"
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weight_str = f"{ds.weight:.2f}"
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# Format details
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detail_parts = []
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for k, v in ds.details.items():
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if k == "component_scores":
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continue
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if isinstance(v, float):
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detail_parts.append(f"{k}: {v:.3f}")
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else:
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detail_parts.append(f"{k}: {v}")
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details_str = "\n".join(detail_parts[:5])
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score_table.add_row(ds.dimension, score_str, weight_str, weighted_str, details_str)
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console.print(score_table)
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console.print()
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# Overall result panel
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overall = result.overall_score
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verdict = result.verdict
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if "GENUINE" in verdict:
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style = "bold green"
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border_style = "green"
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elif "SUSPICIOUS" in verdict:
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style = "bold yellow"
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border_style = "yellow"
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else:
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style = "bold red"
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border_style = "red"
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result_text = Text()
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result_text.append(f"总分 Overall Score: {overall:.3f}\n\n", style="bold")
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result_text.append(f"判定 Verdict: {verdict}\n\n", style=style)
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result_text.append("阈值 Thresholds: ", style="dim")
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result_text.append("≥0.80 ✅ GENUINE", style="green")
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result_text.append(" | ", style="dim")
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result_text.append("≥0.60 ⚠️ SUSPICIOUS", style="yellow")
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result_text.append(" | ", style="dim")
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result_text.append("<0.60 ❌ LIKELY FAKE", style="red")
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console.print(Panel(
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result_text,
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title="🎯 最终判定 / Final Verdict",
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border_style=border_style,
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box=box.HEAVY,
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expand=False,
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padding=(1, 4),
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))
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console.print()
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def _print_performance_summary(genuine_fp: FullFingerprint,
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suspect_fp: FullFingerprint) -> None:
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"""Print performance metrics comparison."""
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perf_table = Table(
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box=box.SIMPLE,
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show_header=True,
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title="⚡ 性能对比 / Performance Comparison",
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)
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perf_table.add_column("指标 Metric", style="bold")
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perf_table.add_column("基准 Genuine", justify="right", style="green")
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perf_table.add_column("待检 Suspect", justify="right", style="yellow")
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gp = genuine_fp.performance
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sp = suspect_fp.performance
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perf_table.add_row(
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"P50 Latency (ms)",
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f"{gp.p50_latency_ms:.1f}",
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f"{sp.p50_latency_ms:.1f}",
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)
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perf_table.add_row(
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"P95 Latency (ms)",
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f"{gp.p95_latency_ms:.1f}",
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f"{sp.p95_latency_ms:.1f}",
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)
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perf_table.add_row(
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"Avg TTFT (ms)",
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f"{gp.avg_ttft_ms:.1f}",
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f"{sp.avg_ttft_ms:.1f}",
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)
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perf_table.add_row(
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"Avg TPS",
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f"{gp.avg_tps:.1f}",
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f"{sp.avg_tps:.1f}",
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)
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perf_table.add_row(
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"Avg Response Len",
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f"{gp.avg_response_length:.0f}",
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f"{sp.avg_response_length:.0f}",
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)
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console.print(perf_table)
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def _print_identity_summary(genuine_fp: FullFingerprint,
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suspect_fp: FullFingerprint) -> None:
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"""Print identity verification comparison."""
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gi = genuine_fp.identity
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si = suspect_fp.identity
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# Identity overview table
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id_table = Table(
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box=box.ROUNDED,
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show_header=True,
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title="🆔 模型身份验证 / Model Identity Verification",
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title_style="bold",
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)
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id_table.add_column("检测项 Check", style="bold")
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id_table.add_column("基准 Genuine", justify="center", style="green")
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id_table.add_column("待检 Suspect", justify="center", style="yellow")
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id_table.add_column("状态 Status", justify="center")
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# Claimed identity
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g_claim = gi.claimed_identity or "unknown"
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s_claim = si.claimed_identity or "unknown"
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claim_status = "[green]✓ 一致[/green]" if g_claim == s_claim else "[red]✗ 不一致[/red]"
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id_table.add_row("声称身份 / Claimed Model", g_claim, s_claim, claim_status)
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# Claimed developer
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g_dev = gi.claimed_developer or "unknown"
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s_dev = si.claimed_developer or "unknown"
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dev_status = "[green]✓ 一致[/green]" if g_dev == s_dev else "[red]✗ 不一致[/red]"
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id_table.add_row("声称开发者 / Developer", g_dev, s_dev, dev_status)
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# Detected model
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g_det = gi.detected_model or "unknown"
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s_det = si.detected_model or "unknown"
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det_status = "[green]✓ 一致[/green]" if g_det == s_det else "[red]✗ 不一致[/red]"
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id_table.add_row("检测到模型 / Detected Model", g_det, s_det, det_status)
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# Detection confidence
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g_conf = f"{gi.detection_confidence:.2f}"
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s_conf = f"{si.detection_confidence:.2f}"
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id_table.add_row("检测置信度 / Confidence", g_conf, s_conf, "")
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# Identity consistency
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g_cons = f"{gi.identity_consistency:.2f}"
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s_cons = f"{si.identity_consistency:.2f}"
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id_table.add_row("身份一致性 / Consistency", g_cons, s_cons, "")
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# Is claimed model
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g_is = "[green]✓ 是[/green]" if gi.is_claimed_model else "[red]✗ 否[/red]"
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s_is = "[green]✓ 是[/green]" if si.is_claimed_model else "[red]✗ 否[/red]"
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id_table.add_row("是否为声称模型 / Is Claimed", g_is, s_is, "")
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# System prompt leaked
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g_leak = "[red]⚠ 泄露[/red]" if gi.system_prompt_leaked else "[green]✓ 安全[/green]"
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s_leak = "[red]⚠ 泄露[/red]" if si.system_prompt_leaked else "[green]✓ 安全[/green]"
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id_table.add_row("系统提示词 / Sys Prompt", g_leak, s_leak, "")
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console.print(id_table)
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# Model scores comparison
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if gi.model_scores or si.model_scores:
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console.print()
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score_table = Table(
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box=box.SIMPLE,
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show_header=True,
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title="📊 模型可能性评分 / Model Probability Scores",
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)
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score_table.add_column("模型 Model", style="bold")
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score_table.add_column("基准 Genuine", justify="right", style="green")
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score_table.add_column("待检 Suspect", justify="right", style="yellow")
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all_models = sorted(set(list(gi.model_scores.keys()) + list(si.model_scores.keys())))
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for model in all_models:
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g_score = gi.model_scores.get(model, 0.0)
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s_score = si.model_scores.get(model, 0.0)
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g_bar = _make_score_bar(g_score)
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s_bar = _make_score_bar(s_score)
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score_table.add_row(model, f"{g_bar} {g_score:.3f}", f"{s_bar} {s_score:.3f}")
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console.print(score_table)
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# Vocab markers
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if gi.vocab_markers or si.vocab_markers:
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console.print()
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vocab_table = Table(
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box=box.SIMPLE,
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show_header=True,
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title="🔤 词汇标记检测 / Vocabulary Markers",
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)
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vocab_table.add_column("模型特征 Model Style", style="bold")
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vocab_table.add_column("基准 Genuine", justify="right", style="green")
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vocab_table.add_column("待检 Suspect", justify="right", style="yellow")
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all_markers = sorted(set(list(gi.vocab_markers.keys()) + list(si.vocab_markers.keys())))
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for marker_model in all_markers:
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g_cnt = gi.vocab_markers.get(marker_model, 0)
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s_cnt = si.vocab_markers.get(marker_model, 0)
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vocab_table.add_row(f"{marker_model} 特征词", str(g_cnt), str(s_cnt))
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console.print(vocab_table)
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# Signature behaviors
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if gi.signature_behaviors or si.signature_behaviors:
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console.print()
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beh_table = Table(
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box=box.SIMPLE,
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show_header=True,
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title="🧬 行为签名 / Signature Behaviors",
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)
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beh_table.add_column("测试 Test", style="bold")
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beh_table.add_column("基准 Genuine", style="green")
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beh_table.add_column("待检 Suspect", style="yellow")
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all_tests = sorted(set(list(gi.signature_behaviors.keys()) + list(si.signature_behaviors.keys())))
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for test in all_tests:
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g_val = gi.signature_behaviors.get(test, "N/A")
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s_val = si.signature_behaviors.get(test, "N/A")
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beh_table.add_row(test, str(g_val)[:50], str(s_val)[:50])
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console.print(beh_table)
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# Mismatch alerts
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if si.identity_mismatch_reasons:
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console.print()
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alert_text = Text()
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alert_text.append("⚠️ 待检渠道身份异常 / Suspect Identity Alerts:\n\n", style="bold red")
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for i, reason in enumerate(si.identity_mismatch_reasons, 1):
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alert_text.append(f" {i}. {reason}\n", style="red")
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console.print(Panel(
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alert_text,
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title="🚨 身份告警 / Identity Alerts",
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border_style="red",
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box=box.HEAVY,
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expand=False,
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padding=(1, 2),
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))
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if gi.identity_mismatch_reasons:
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console.print()
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alert_text = Text()
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alert_text.append("⚠️ 基准渠道身份异常 / Genuine Identity Alerts:\n\n", style="bold yellow")
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for i, reason in enumerate(gi.identity_mismatch_reasons, 1):
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alert_text.append(f" {i}. {reason}\n", style="yellow")
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console.print(Panel(
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alert_text,
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title="⚠️ 基准告警 / Genuine Alerts",
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border_style="yellow",
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box=box.HEAVY,
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expand=False,
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padding=(1, 2),
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))
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# System prompt leak details
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if si.system_prompt_leaked and si.system_prompt_hints:
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console.print()
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leak_text = Text()
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leak_text.append("🔓 待检渠道系统提示词泄露 / Suspect System Prompt Leak:\n\n", style="bold red")
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for hint in si.system_prompt_hints[:5]:
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leak_text.append(f" → {hint}\n", style="red")
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console.print(Panel(
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leak_text,
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title="🔓 系统提示词泄露 / System Prompt Leak",
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border_style="red",
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box=box.HEAVY,
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expand=False,
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padding=(1, 2),
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))
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def _make_score_bar(score: float, width: int = 10) -> str:
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"""Create a simple text-based score bar."""
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filled = int(score * width)
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empty = width - filled
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return "█" * filled + "░" * empty
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def save_json_report(result: ComparisonResult,
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genuine_fp: FullFingerprint,
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suspect_fp: FullFingerprint,
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output_dir: str = "results") -> str:
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"""Save comparison results to a JSON file. Returns the file path."""
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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filename = f"comparison_{timestamp}.json"
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filepath = output_path / filename
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report = {
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"metadata": {
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"tool": "AI API Fingerprint Detector",
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"version": "2.0.0",
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"timestamp": result.timestamp,
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},
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"channels": {
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"genuine": genuine_fp.channel_name,
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"suspect": suspect_fp.channel_name,
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},
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"result": {
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"overall_score": result.overall_score,
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"verdict": result.verdict,
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"dimension_scores": [ds.to_dict() for ds in result.dimension_scores],
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},
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"fingerprints": {
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"genuine": genuine_fp.to_dict(),
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"suspect": suspect_fp.to_dict(),
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},
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}
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with open(filepath, 'w', encoding='utf-8') as f:
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json.dump(report, f, ensure_ascii=False, indent=2, default=str)
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console.print(f"[dim]📁 JSON report saved to: {filepath}[/dim]")
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return str(filepath)
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