File size: 17,493 Bytes
780a87a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
d23039a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fba30db
d23039a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
415b879
d23039a
 
 
 
 
 
 
 
 
 
 
fba30db
 
d23039a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
415b879
d23039a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bc6669a
d23039a
 
 
bc6669a
d23039a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
bc6669a
d23039a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
<!--
  ⚠️  DEPRECATED: HTML Report Template
  
  This template is no longer used. Report generation now uses ReportLab directly
  from backend/services/report_service.py for better control over PDF rendering,
  typography, tables, images, and hyperlinks.
  
  KEPT FOR REFERENCE: If future maintainers wish to refactor to weasyprint
  (HTML β†’ PDF via Cairo), this template provides the HTML contract. All styling,
  sections, and data bindings are documented here.
  
  Current System (ReportLab):
  - Pros: Pure Python, fast, reliable image/table handling, no external C deps
  - Cons: API-driven (not designer-friendly)
  
  Future Option (weasyprint):
  - Pros: Designer-friendly CSS, reuse this template, complex layouts easy
  - Cons: Slower, requires Cairo/Pango system libs, heavier deployment
  
  Timeline: Keep this until weasyprint migration is planned (Phase TBD).
  Last used: Never in production (scaffolded but ReportLab chosen instead)
  Last updated: 2026-05-05
-->

<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8" />
  <title>DeepShield Analysis Report β€” {{ analysis_id }}</title>
  <style>
    @page { size: A4; margin: 16mm 18mm; }
    body { font-family: Helvetica, Arial, sans-serif; color: #1A202C; font-size: 10pt; line-height: 1.45; }

    /* ── Typography ── */
    h1 { color: #4F46E5; margin: 0 0 2pt 0; font-size: 18pt; letter-spacing: -0.3pt; }
    h2 { color: #4F46E5; margin: 14pt 0 5pt 0; font-size: 12pt; border-bottom: 1pt solid #E5E7EB; padding-bottom: 2pt; }
    h3 { margin: 10pt 0 4pt 0; font-size: 10.5pt; color: #374151; }
    .muted { color: #6B7280; font-size: 8.5pt; }

    /* ── Header / logo row ── */
    .header-table { width: 100%; border-collapse: collapse; border-bottom: 2pt solid #4F46E5; padding-bottom: 6pt; margin-bottom: 10pt; }
    .logo-cell { font-size: 22pt; font-weight: bold; color: #4F46E5; width: 120pt; white-space: nowrap; padding-right: 8pt; }
    .logo-shield { color: #6366F1; }
    .meta-cell { font-size: 8.5pt; color: #6B7280; vertical-align: bottom; }

    /* ── Verdict row ── */
    .verdict-table { width: 100%; border-collapse: collapse; margin: 6pt 0 10pt 0; background: #F9FAFB; }
    .verdict-score-cell { width: 90pt; text-align: center; vertical-align: middle; padding: 8pt; }
    .score-num { font-size: 26pt; font-weight: bold; }
    .score-denom { font-size: 9pt; color: #6B7280; }
    .score.real { color: #43A047; }
    .score.warn { color: #FB8C00; }
    .score.fake { color: #E53935; }
    .verdict-detail-cell { padding: 8pt 10pt; vertical-align: middle; }
    .verdict-label { font-size: 13pt; font-weight: bold; color: #1A202C; }
    .verdict-sub { font-size: 8.5pt; color: #6B7280; margin-top: 2pt; }
    .donut-cell { width: 75pt; text-align: center; vertical-align: middle; padding: 4pt; }
    .donut-cell img { width: 72pt; }

    /* ── LLM card ── */
    .llm-box { background: #EEF2FF; padding: 7pt 9pt; margin: 6pt 0; border-radius: 2pt; }
    .llm-para { font-size: 9.5pt; color: #1A202C; margin: 0 0 5pt 0; }
    .llm-bullets { margin: 0; padding-left: 14pt; }
    .llm-bullets li { font-size: 9pt; color: #374151; margin-bottom: 2pt; }

    /* ── Tables ── */
    table.data { width: 100%; border-collapse: collapse; margin: 5pt 0; }
    table.data th { background: #F3F4F6; color: #374151; font-size: 8.5pt; text-align: left; padding: 3pt 6pt; border-bottom: 1pt solid #E5E7EB; }
    table.data td { font-size: 9pt; padding: 3pt 6pt; border-bottom: 1pt solid #F3F4F6; vertical-align: top; }
    table.data tr:last-child td { border-bottom: none; }

    /* ── VLM breakdown ── */
    .vlm-score-bar-wrap { background: #E5E7EB; height: 5pt; width: 70pt; display: block; overflow: hidden; }
    .vlm-score-bar { height: 5pt; display: block; }
    .vlm-real { background: #43A047; }
    .vlm-warn { background: #FB8C00; }
    .vlm-fake { background: #E53935; }

    /* ── Badges ── */
    .badge { display: inline-block; padding: 1pt 5pt; border-radius: 3pt; font-size: 8pt; font-weight: bold; }
    .sev-high { background: #FEE2E2; color: #B91C1C; }
    .sev-medium { background: #FEF3C7; color: #92400E; }
    .sev-low { background: #DBEAFE; color: #1E40AF; }
    .badge-green { background: #DCFCE7; color: #166534; }
    .badge-red   { background: #FEE2E2; color: #991B1B; }

    /* ── Keywords ── */
    .keyword { display: inline-block; background: #EEF2FF; color: #4F46E5; padding: 1pt 6pt; border-radius: 3pt; margin: 1pt; font-size: 8.5pt; }

    /* ── Truth-override ── */
    .truth-box { background: #DCFCE7; padding: 5pt 8pt; margin: 5pt 0; font-size: 9pt; border-radius: 2pt; }

    /* ── Footer ── */
    .footer { margin-top: 16pt; padding-top: 5pt; border-top: 1pt solid #E5E7EB; color: #9CA3AF; font-size: 8pt; }
  </style>
</head>
<body>

  {# ── Header ── #}
  <table class="header-table">
    <tr>
      <td class="logo-cell"><span class="logo-shield">&#9646;</span> DeepShield</td>
      <td class="meta-cell">
        Analysis Report &nbsp;Β·&nbsp; ID: {{ analysis_id }}<br />
        Media: <b>{{ media_type | upper }}</b> &nbsp;Β·&nbsp; Generated: {{ generated_at }}
      </td>
    </tr>
  </table>

  {# ── Verdict ── #}
  <h2>Deepfake Probability</h2>
  <table class="verdict-table">
    <tr>
      <td class="verdict-score-cell">
        <div class="score-num score {{ score_class }}">{{ fake_score }}</div>
        <div class="score-denom">/ 100</div>
      </td>
      <td class="verdict-detail-cell">
        <div class="verdict-label">{{ verdict.label }}</div>
        <div class="verdict-sub">Severity: {{ verdict.severity }}</div>
        <div class="verdict-sub">Model: {{ verdict.model_label }} &nbsp;({{ '%.1f' | format(verdict.model_confidence * 100) }}% confidence)</div>
      </td>
      {% if donut_b64 %}
      <td class="donut-cell">
        <img src="data:image/png;base64,{{ donut_b64 }}" alt="score donut" />
      </td>
      {% endif %}
    </tr>
  </table>

  {# ── LLM Explanation ── #}
  {% if llm_summary and llm_summary.paragraph %}
  <h2>AI Explanation</h2>
  <div class="llm-box">
    <p class="llm-para">{{ llm_summary.paragraph }}</p>
    {% if llm_summary.bullets %}
    <ul class="llm-bullets">
      {% for b in llm_summary.bullets %}<li>{{ b }}</li>{% endfor %}
    </ul>
    {% endif %}
    {% if llm_summary.model_used %}
    <div class="muted" style="margin-top:4pt;">via {{ llm_summary.model_used }}</div>
    {% endif %}
  </div>
  {% endif %}

  {# ══════════ IMAGE ══════════ #}
  {% if media_type == 'image' %}

    {# EXIF #}
    {% if explainability.exif %}
    <h2>EXIF Metadata</h2>
    <table class="data">
      <tr><th>Field</th><th>Value</th><th>Trust Signal</th></tr>
      {% if explainability.exif.make %}
      <tr><td>Camera Make</td><td>{{ explainability.exif.make }}</td><td><span class="badge badge-green">+real</span></td></tr>
      {% endif %}
      {% if explainability.exif.model %}
      <tr><td>Camera Model</td><td>{{ explainability.exif.model }}</td><td></td></tr>
      {% endif %}
      {% if explainability.exif.datetime_original %}
      <tr><td>Date Taken</td><td>{{ explainability.exif.datetime_original }}</td><td><span class="badge badge-green">+real</span></td></tr>
      {% endif %}
      {% if explainability.exif.software %}
      <tr><td>Software</td><td>{{ explainability.exif.software }}</td>
        <td>{% if 'photoshop' in explainability.exif.software | lower %}<span class="badge badge-red">+fake</span>{% endif %}</td></tr>
      {% endif %}
      {% if explainability.exif.lens_model %}
      <tr><td>Lens Model</td><td>{{ explainability.exif.lens_model }}</td><td></td></tr>
      {% endif %}
      {% if explainability.exif.gps_info %}
      <tr><td>GPS</td><td>{{ explainability.exif.gps_info }}</td><td></td></tr>
      {% endif %}
      <tr>
        <td colspan="2"><b>Trust adjustment</b></td>
        <td>
          {% if explainability.exif.trust_adjustment > 0 %}
            <span class="badge badge-red">+{{ explainability.exif.trust_adjustment }} (fake signal)</span>
          {% elif explainability.exif.trust_adjustment < 0 %}
            <span class="badge badge-green">{{ explainability.exif.trust_adjustment }} (real signal)</span>
          {% else %}
            neutral
          {% endif %}
        </td>
      </tr>
    </table>
    {% endif %}

    {# Artifact indicators #}
    {% if explainability.artifact_indicators %}
    <h2>Artifact Indicators</h2>
    <table class="data">
      <tr><th>Type</th><th>Severity</th><th>Confidence</th><th>Description</th></tr>
      {% for ind in explainability.artifact_indicators %}
      <tr>
        <td>{{ ind.type }}</td>
        <td><span class="badge sev-{{ ind.severity }}">{{ ind.severity }}</span></td>
        <td>{{ '%.0f' | format(ind.confidence * 100) }}%</td>
        <td>{{ ind.description }}</td>
      </tr>
      {% endfor %}
    </table>
    {% else %}
    <h2>Artifact Indicators</h2>
    <div class="muted">No artifacts detected.</div>
    {% endif %}

    {# VLM Detailed Breakdown #}
    {% if explainability.vlm_breakdown %}
    <h2>Detailed Breakdown</h2>
    {% if explainability.vlm_breakdown.model_used %}
    <div class="muted" style="margin-bottom:5pt;">Scored by {{ explainability.vlm_breakdown.model_used }}</div>
    {% endif %}
    <table class="data">
      <tr><th>Component</th><th>Score</th><th>Bar</th><th>Notes</th></tr>
      {% set bd = explainability.vlm_breakdown %}
      {% for comp_key, comp_label in [
          ('facial_symmetry',      'Facial Symmetry'),
          ('skin_texture',         'Skin Texture'),
          ('lighting_consistency', 'Lighting Consistency'),
          ('background_coherence', 'Background Coherence'),
          ('anatomy_hands_eyes',   'Anatomy / Hands & Eyes'),
          ('context_objects',      'Context & Objects')
      ] %}
        {% set comp = bd[comp_key] %}
        {% set sc2 = comp.score if comp else 75 %}
        {% set bar_cls = 'vlm-real' if sc2 >= 70 else ('vlm-warn' if sc2 >= 40 else 'vlm-fake') %}
      <tr>
        <td>{{ comp_label }}</td>
        <td><b>{{ sc2 }}</b>/100</td>
        <td>
          <span class="vlm-score-bar-wrap">
            <span class="vlm-score-bar {{ bar_cls }}" style="display: block; width: {{ sc2 }}%;"></span>
          </span>
        </td>
        <td class="muted">{{ comp.notes if comp else '' }}</td>
      </tr>
      {% endfor %}
    </table>
    {% endif %}

  {% endif %}{# end image #}

  {# ══════════ VIDEO ══════════ #}
  {% if media_type == 'video' %}
  <h2>Frame-Level Analysis</h2>
  <table class="data">
    <tr><th>Metric</th><th>Value</th></tr>
    <tr><td>Frames sampled</td><td>{{ explainability.num_frames_sampled }}</td></tr>
    <tr><td>Frames with face</td><td>{{ explainability.num_face_frames }}</td></tr>
    <tr><td>Suspicious frames</td><td>{{ explainability.num_suspicious_frames }}</td></tr>
    <tr><td>Mean suspicious prob</td><td>{{ '%.1f' | format(explainability.mean_suspicious_prob * 100) }}%</td></tr>
    <tr><td>Max suspicious prob</td><td>{{ '%.1f' | format(explainability.max_suspicious_prob * 100) }}%</td></tr>
    <tr><td>Insufficient faces</td><td>{{ explainability.insufficient_faces }}</td></tr>
  </table>
  {% endif %}

  {# ══════════ TEXT ══════════ #}
  {% if media_type == 'text' %}

    {# Language + truth-override #}
    {% if explainability.detected_language and explainability.detected_language != 'en' %}
    <h2>Language</h2>
    <div class="muted">Detected: <b>{{ explainability.detected_language | upper }}</b> β€” analysed via multilingual model</div>
    {% endif %}
    {% if explainability.truth_override and explainability.truth_override.applied %}
    <div class="truth-box">
      <b>Truth-override applied.</b>
      Corroborated by {{ explainability.truth_override.source_name }}
      ({{ '%.0f' | format(explainability.truth_override.similarity * 100) }}% similarity).
      Fake probability reduced from {{ '%.1f' | format(explainability.truth_override.fake_prob_before * 100) }}%
      to {{ '%.1f' | format(explainability.truth_override.fake_prob_after * 100) }}%.
    </div>
    {% endif %}

    <h2>Text Classification</h2>
    <table class="data">
      <tr><th>Metric</th><th>Value</th></tr>
      <tr><td>Fake probability</td><td>{{ '%.1f' | format(explainability.fake_probability * 100) }}%</td></tr>
      <tr><td>Top label</td><td>{{ explainability.top_label }}</td></tr>
      <tr><td>Sensationalism score</td><td>{{ explainability.sensationalism.score }}/100 ({{ explainability.sensationalism.level }})</td></tr>
      <tr><td>Exclamations</td><td>{{ explainability.sensationalism.exclamation_count }}</td></tr>
      <tr><td>ALL CAPS words</td><td>{{ explainability.sensationalism.caps_word_count }}</td></tr>
      <tr><td>Clickbait matches</td><td>{{ explainability.sensationalism.clickbait_matches }}</td></tr>
      <tr><td>Emotional words</td><td>{{ explainability.sensationalism.emotional_word_count }}</td></tr>
    </table>

    {% if explainability.manipulation_indicators %}
    <h3>Manipulation Indicators ({{ explainability.manipulation_indicators | length }})</h3>
    <table class="data">
      <tr><th>Pattern</th><th>Severity</th><th>Matched text</th></tr>
      {% for m in explainability.manipulation_indicators %}
      <tr>
        <td>{{ m.pattern_type }}</td>
        <td><span class="badge sev-{{ m.severity }}">{{ m.severity }}</span></td>
        <td>{{ m.matched_text }}</td>
      </tr>
      {% endfor %}
    </table>
    {% endif %}

    {% if explainability.keywords %}
    <h3>Extracted Keywords</h3>
    <div>{% for kw in explainability.keywords %}<span class="keyword">{{ kw }}</span>{% endfor %}</div>
    {% endif %}

  {% endif %}{# end text #}

  {# ══════════ SCREENSHOT ══════════ #}
  {% if media_type == 'screenshot' %}

    {% if explainability.detected_language and explainability.detected_language != 'en' %}
    <div class="muted" style="margin-bottom:4pt;">Detected language: <b>{{ explainability.detected_language | upper }}</b></div>
    {% endif %}
    {% if explainability.truth_override and explainability.truth_override.applied %}
    <div class="truth-box">
      <b>Truth-override applied.</b> {{ explainability.truth_override.source_name }}
      ({{ '%.0f' | format(explainability.truth_override.similarity * 100) }}% similarity)
    </div>
    {% endif %}

    <h2>Extracted Text</h2>
    <div class="muted">{{ explainability.ocr_boxes | length }} OCR regions detected</div>
    <table class="data">
      <tr><td style="white-space:pre-wrap; font-size:8.5pt; padding:6pt;">{{ explainability.extracted_text }}</td></tr>
    </table>

    <h3>Analysis Summary</h3>
    <table class="data">
      <tr><th>Metric</th><th>Value</th></tr>
      <tr><td>Fake probability</td><td>{{ '%.1f' | format(explainability.fake_probability * 100) }}%</td></tr>
      <tr><td>Sensationalism</td><td>{{ explainability.sensationalism.score }}/100 ({{ explainability.sensationalism.level }})</td></tr>
      <tr><td>Suspicious phrases</td><td>{{ explainability.suspicious_phrases | length }}</td></tr>
      <tr><td>Layout anomalies</td><td>{{ explainability.layout_anomalies | length }}</td></tr>
    </table>

    {% if explainability.suspicious_phrases %}
    <h3>Suspicious Phrases</h3>
    <table class="data">
      <tr><th>Text</th><th>Pattern</th><th>Severity</th></tr>
      {% for p in explainability.suspicious_phrases %}
      <tr>
        <td>{{ p.text }}</td>
        <td>{{ p.pattern_type }}</td>
        <td><span class="badge sev-{{ p.severity }}">{{ p.severity }}</span></td>
      </tr>
      {% endfor %}
    </table>
    {% endif %}

  {% endif %}{# end screenshot #}

  {# ══════════ SOURCES (all types) ══════════ #}
  {% if trusted_sources %}
  <h2>Trusted Source Cross-Reference ({{ trusted_sources | length }})</h2>
  <table class="data">
    <tr><th>Source</th><th>Title</th><th>Relevance</th></tr>
    {% for s in trusted_sources %}
    <tr>
      <td>{{ s.source_name }}</td>
      <td>{{ s.title }}</td>
      <td>{{ '%.0f' | format(s.relevance_score * 100) }}%</td>
    </tr>
    {% endfor %}
  </table>
  {% endif %}

  {% if contradicting_evidence %}
  <h2 style="color:#B91C1C;">Contradicting Evidence ({{ contradicting_evidence | length }})</h2>
  <table class="data">
    <tr><th>Source</th><th>Title</th><th>Type</th></tr>
    {% for c in contradicting_evidence %}
    <tr><td>{{ c.source_name }}</td><td>{{ c.title }}</td><td>{{ c.type }}</td></tr>
    {% endfor %}
  </table>
  {% endif %}

  {# ══════════ PROCESSING ══════════ #}
  <h2>Processing Summary</h2>
  <div class="muted">Model: {{ processing_summary.model_used }} &nbsp;Β·&nbsp; Duration: {{ processing_summary.total_duration_ms }} ms</div>
  <div style="font-size:8.5pt; margin-top:3pt;">{{ processing_summary.stages_completed | join(' β†’ ') }}</div>

  {# ══════════ FOOTER ══════════ #}
  <div class="footer">
    <b>DeepShield Responsible-AI Notice.</b> {{ responsible_ai_notice }}<br />
    Generated {{ generated_at }}. Report expires in 1 hour.
    AI-assisted analysis β€” cross-check with trusted sources before sharing.
  </div>

</body>
</html>