The workflow JSON
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{
"name": "fakey",
"nodes": [
{
"parameters": {
"httpMethod": "POST",
"path": "check-image",
"responseMode": "responseNode",
"options": {}
},
"id": "2a0037d8-46b4-42a2-82ea-2f36d368ad0e",
"name": "\ud83d\udce5 Receive Image + Claims",
"type": "n8n-nodes-base.webhook",
"typeVersion": 2,
"position": [
-3640,
-80
]
},
{
"parameters": {
"jsCode": "// \u2500\u2500 Extract binary + parse EXIF \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nconst binaryData = $input.item.binary;\nconst body = $input.item.json;\n\nif (!binaryData || Object.keys(binaryData).length === 0) {\n throw new Error('No image received. In Postman: Body \u2192 form-data \u2192 key \"data\" \u2192 type File.');\n}\n\nconst fieldName = Object.keys(binaryData)[0];\nconst field = binaryData[fieldName];\nif (!field.data) throw new Error('File received but has no data.');\n\nconst base64 = field.data;\nconst mimeType = field.mimeType || 'image/jpeg';\nconst fileName = field.fileName || 'image.jpg';\nconst caption = body?.caption || null;\nconst claimDate = body?.claimDate || null;\n\n// \u2500\u2500 EXIF Parsing \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nconst bytes = Buffer.from(base64, 'base64');\nlet exif = {\n hasExif: false, fileType: 'Unknown',\n dateTimeOriginal: null, modifyDate: null, wasEdited: false,\n make: null, software: null, gpsFound: false\n};\n\nconst AI_TOOLS = [\n 'Stable Diffusion','Midjourney','DALL-E','DALL\u00b7E','Adobe Firefly',\n 'Canva','Runway','Pika','NovelAI','DreamStudio','ComfyUI','Automatic1111',\n 'Bing Image Creator'\n];\nconst CAMERAS = [\n 'Canon','Nikon','Sony','Apple','Samsung','Google','Huawei',\n 'OnePlus','Xiaomi','OPPO','Fujifilm','Panasonic','Leica','DJI','GoPro'\n];\n\ntry {\n const isJpeg = bytes[0]===0xFF && bytes[1]===0xD8;\n const isPng = bytes[0]===0x89 && bytes[1]===0x50;\n\n if (isJpeg) {\n exif.fileType = 'JPEG';\n const str = bytes.toString('binary');\n const exifIdx = str.indexOf('Exif');\n if (exifIdx !== -1) {\n exif.hasExif = true;\n const chunk = str.substring(exifIdx, exifIdx + 2048);\n const printable = chunk.replace(/[^\\x20-\\x7E]/g,'|').split('|').filter(s=>s.length>=3);\n const dates = chunk.match(/(\\d{4}):(\\d{2}):(\\d{2}) (\\d{2}):(\\d{2}):(\\d{2})/g) || [];\n if (dates[0]) exif.dateTimeOriginal = dates[0].replace(/(\\d{4}):(\\d{2}):(\\d{2})/,'$1-$2-$3');\n if (dates[1]) {\n exif.modifyDate = dates[1].replace(/(\\d{4}):(\\d{2}):(\\d{2})/,'$1-$2-$3');\n exif.wasEdited = dates[0] !== dates[1];\n }\n exif.gpsFound = chunk.includes('GPS');\n for (const s of printable) {\n for (const ai of AI_TOOLS) {\n if (s.toLowerCase().includes(ai.toLowerCase())) { exif.software=ai; break; }\n }\n if (exif.software) break;\n }\n for (const s of printable) {\n for (const cam of CAMERAS) {\n if (s.toLowerCase().includes(cam.toLowerCase()) && !exif.make) { exif.make=s.trim().substring(0,40); }\n }\n }\n }\n }\n if (isPng) {\n exif.fileType = 'PNG';\n const str = bytes.toString('binary');\n for (const ai of AI_TOOLS) { if (str.includes(ai)) { exif.software=ai; exif.hasExif=true; break; } }\n if (str.includes('Steps:')||str.includes('Sampler:')||str.includes('parameters')) {\n if (!exif.software) exif.software='AI Generator (SD metadata)';\n exif.hasExif = true;\n }\n }\n} catch(e) { exif.parseError = e.message; }\n\nconst currentYear = new Date().getFullYear();\nlet exifAgeWarning = null;\nlet exifInsight = 'No EXIF data \u2014 likely screenshot or downloaded.';\n\nif (exif.software) {\n exifInsight = `\ud83d\udea8 AI SOFTWARE IN METADATA: \"${exif.software}\"`;\n} else if (exif.wasEdited) {\n exifInsight = `\u26a0\ufe0f File modified after creation. Created: ${exif.dateTimeOriginal} | Modified: ${exif.modifyDate}`;\n} else if (exif.dateTimeOriginal) {\n const yr = parseInt(exif.dateTimeOriginal.substring(0,4));\n if (!isNaN(yr) && yr < currentYear-2) {\n exifAgeWarning = `Photo taken in ${yr} \u2014 ${currentYear-yr} years ago.`;\n exifInsight = `\u26a0\ufe0f ${exifAgeWarning}`;\n } else {\n exifInsight = `Photo taken: ${exif.dateTimeOriginal}${exif.make?' | Camera: '+exif.make:''}`;\n }\n} else if (exif.hasExif) {\n exifInsight = `EXIF found${exif.make?' | Camera: '+exif.make:''} | No date recorded.`;\n}\n\nreturn [{\n json: { base64, mimeType, fileName, caption, claimDate, exif, exifAgeWarning, exifInsight, currentYear },\n binary: { data: { ...field } }\n}];"
},
"id": "ff49398b-b022-4d33-a818-4563c8cd6e58",
"name": "\ud83d\udce6 Extract Base64 + EXIF",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-3420,
-80
]
},
{
"parameters": {
"method": "POST",
"url": "https://router.huggingface.co/hf-inference/models/Wvolf/ViT_Deepfake_Detection",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "Bearer Your Hugging Face Token"
},
{
"name": "Content-Type",
"value": "application/octet-stream"
},
{
"name": "x-wait-for-model",
"value": "true"
}
]
},
"sendBody": true,
"contentType": "binaryData",
"inputDataFieldName": "data",
"options": {}
},
"id": "b8c13a46-41a1-4935-a23a-72d0d36a66b1",
"name": "\ud83e\udd16 Wvolf \u2014 Deepfake",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-3200,
-80
]
},
{
"parameters": {
"jsCode": "const wvolfRaw = $input.item.json;\nconst prev = $('\ud83d\udce6 Extract Base64 + EXIF').first();\n\nlet deepfakeScore = 50;\n\n// Handle BOTH: single object {label, score} OR array [{label,score}]\nconst entries = Array.isArray(wvolfRaw) ? wvolfRaw : [wvolfRaw];\n\nconst fake = entries.find(r => r.label?.toLowerCase() === 'fake');\nconst real = entries.find(r => r.label?.toLowerCase() === 'real');\n\nif (fake) deepfakeScore = Math.round(fake.score * 100);\nelse if (real) deepfakeScore = Math.round((1 - real.score) * 100);\nelse deepfakeScore = -1;\n\nreturn [{\n json: { ...prev.json, deepfakeScore },\n binary: prev.binary\n}];"
},
"id": "fe11e08c-484f-4eba-b674-35b89c88a2ed",
"name": "\ud83d\udcbe Save Wvolf Score",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-2980,
-80
]
},
{
"parameters": {
"method": "POST",
"url": "https://router.huggingface.co/hf-inference/models/dima806/ai_vs_real_image_detection",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "Bearer Your Hugging Face Token"
},
{
"name": "Content-Type",
"value": "application/octet-stream"
},
{
"name": "x-wait-for-model",
"value": "true"
}
]
},
"sendBody": true,
"contentType": "binaryData",
"inputDataFieldName": "data",
"options": {}
},
"id": "c9efdcb7-0145-4a5b-985f-dba42fbca72e",
"name": "\ud83d\uddbc\ufe0f dima806 \u2014 AI Gen",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-2760,
-80
]
},
{
"parameters": {
"jsCode": "const dima806Raw = $input.item.json;\nconst prev = $('\ud83d\udcbe Save Wvolf Score').first();\n\nlet aiGenScore = 50;\n\n// Handle BOTH: single object OR array\nconst entries = Array.isArray(dima806Raw) ? dima806Raw : [dima806Raw];\n\nconst fake = entries.find(r => r.label?.toUpperCase() === 'FAKE');\nconst real = entries.find(r => r.label?.toUpperCase() === 'REAL');\n\nif (fake) aiGenScore = Math.round(fake.score * 100);\nelse if (real) aiGenScore = Math.round((1 - real.score) * 100);\nelse aiGenScore = -1;\n\nreturn [{\n json: { ...prev.json, aiGenScore },\n binary: prev.binary\n}];"
},
"id": "cca75688-bf53-452a-b63b-e5bfee8c67c2",
"name": "\ud83d\udcbe Save dima806 Score",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-2540,
-80
]
},
{
"parameters": {
"method": "POST",
"url": "https://api.imgbb.com/1/upload",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "key",
"value": "Your ImgBB Key"
}
]
},
"sendBody": true,
"contentType": "multipart-form-data",
"bodyParameters": {
"parameters": [
{
"name": "image",
"value": "={{ $json.base64 }}"
},
{
"name": "name",
"value": "={{ $json.fileName }}"
}
]
},
"options": {}
},
"id": "25135125-ab99-45d3-ab56-b63c37e2443f",
"name": "\ud83c\udf10 imgbb \u2014 Public URL",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-2320,
-80
]
},
{
"parameters": {
"jsCode": "// Save imgbb URL, carry everything forward\nconst imgbbRaw = $input.item.json;\nconst prev = $('\ud83d\udcbe Save dima806 Score').first();\n\nconst imageUrl = imgbbRaw?.data?.url || null;\nif (!imageUrl) throw new Error('imgbb upload failed. Check your IMGBB_KEY_HERE value.');\n\nreturn [{\n json: { ...prev.json, imageUrl }\n}];"
},
"id": "11726f88-27e1-42fb-a110-e40fdfafb487",
"name": "\ud83d\udcbe Save imgbb URL",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-2100,
-80
]
},
{
"parameters": {
"url": "https://serpapi.com/search",
"sendQuery": true,
"queryParameters": {
"parameters": [
{
"name": "engine",
"value": "google_reverse_image"
},
{
"name": "image_url",
"value": "={{ $json.imageUrl }}"
},
{
"name": "api_key",
"value": "Your SerpAPI Key"
}
]
},
"options": {}
},
"id": "c4a87e6e-9e9f-4337-8505-a994addb2d70",
"name": "\ud83d\udd0d SerpAPI \u2014 Reverse Search",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-1880,
-80
],
"onError": "continueRegularOutput"
},
{
"parameters": {
"jsCode": "// \u2500\u2500 All data is now in one place \u2014 build Gemini prompt \u2500\u2500\nconst serpRaw = $input.item.json;\nconst prev = $('\ud83d\udcbe Save imgbb URL').first().json;\n\nconst { base64, mimeType, fileName, caption, claimDate, exif, exifAgeWarning, exifInsight, currentYear, deepfakeScore, aiGenScore } = prev;\n\n// \u2500\u2500 Parse SerpAPI (graceful \u2014 handles 503, error objects, empty results) \u2500\u2500\n// When SerpAPI fails + \"Continue on Error\" is set, n8n passes error object\n// We safely fall back to empty results in all failure cases\nconst imageResults = Array.isArray(serpRaw?.image_results) ? serpRaw.image_results : [];\nconst totalResults = imageResults.length;\nconst topSources = imageResults.slice(0,5).map(r => ({\n source: r.source || r.link || 'Unknown',\n title: r.title || 'Unknown',\n snippet: r.snippet || ''\n}));\nconst allText = imageResults.map(r=>(r.snippet||'')+' '+(r.title||'')).join(' ');\nconst yearsFound = [...new Set((allText.match(/\\b(19|20)\\d{2}\\b/g)||[]))].sort();\nconst earliestYear = yearsFound.length > 0 ? parseInt(yearsFound[0]) : null;\n\n// \u2500\u2500 Context strings for Gemini \u2500\u2500\nconst hfContext = [\n deepfakeScore >= 0\n ? `Deepfake Face Classifier (Wvolf ViT, 98.7% lab accuracy): ${deepfakeScore}% probability FAKE`\n : 'Deepfake classifier: unavailable',\n aiGenScore >= 0\n ? `AI Generation Classifier (dima806 ViT, 98.25% lab accuracy): ${aiGenScore}% probability AI-GENERATED`\n : 'AI generation classifier: unavailable'\n].join('\\n');\n\nconst exifContext = exif.software\n ? `STRONG SIGNAL \u2014 AI software in EXIF: \"${exif.software}\"`\n : exif.dateTimeOriginal\n ? `Date taken: ${exif.dateTimeOriginal}${exif.make?' | Camera: '+exif.make:''}${exif.wasEdited?' | FILE EDITED AFTER CREATION':''}`\n : 'No EXIF metadata (screenshot or stripped)';\n\nconst serpContext = earliestYear\n ? `Found ${totalResults} prior web appearances. Earliest year: ${earliestYear}. Top sources: ${topSources.slice(0,3).map(s=>s.source).join(', ')}`\n : totalResults>0\n ? `Found ${totalResults} appearances but no specific date found`\n : serpRaw?.error\n ? `Reverse image search unavailable (${serpRaw.error})`\n : 'No prior appearances found in reverse image search';\n\nconst claimContext = (caption||claimDate)\n ? `Caption: \"${caption||'none'}\" | Claimed date: \"${claimDate||'none'}\"`\n : 'No caption or date claim provided';\n\nconst prompt = `You are an expert forensic image analyst. Carefully analyze this image.\n\n\u2550\u2550\u2550 SIGNALS FROM OTHER DETECTION SYSTEMS \u2550\u2550\u2550\n${hfContext}\nEXIF: ${exifContext}\nReverse Search: ${serpContext}\nUser Claim: ${claimContext}\n\n\u2550\u2550\u2550 YOUR FORENSIC TASK \u2550\u2550\u2550\nForm your OWN independent visual judgment. Do NOT just repeat the scores above.\nThen state whether you AGREE or DISAGREE with the classifiers and why.\n\n1. DEEPFAKE SIGNS \u2014 check for:\n- Face boundary artifacts (blurring/halos at jaw/hairline)\n- Skin tone mismatch at face/neck edges\n- Unnatural eye reflections or glassy stare\n- Hair edge artifacts or pixelation at boundary\n- Lighting mismatch between face and background\n- Stiff or unnatural facial expression\n\n2. AI GENERATION SIGNS \u2014 check for:\n- Skin texture too smooth, waxy, or plastic-like\n- Wrong number of fingers or morphed hands\n- Blurry, illegible, or hallucinated text in image\n- Unnaturally perfect facial symmetry\n- Dreamlike background or unnatural bokeh\n- Repeated background patterns\n- Inconsistent or impossible light sources\n\n3. GENERAL INCONSISTENCIES \u2014 check for:\n- Shadows that do not match light direction\n- Objects that look copy-pasted\n- Perspective or scale errors\n- Elements that defy physics\n\n4. CONTEXT CLUES \u2014 check for:\n- Any visible text (news tickers, signs, watermarks, dates)?\n- What technology era does this suggest (phone models, car models, fashion)?\n- Does anything contradict the claimed date \"${claimDate||'not provided'}\"?\n\nReturn ONLY raw JSON. No markdown. No backticks. Nothing else:\n{\n \"deepfake_visual\": { \"score\": <0-100>, \"reasoning\": \"<what you specifically saw or did not see>\" },\n \"ai_gen_visual\": { \"score\": <0-100>, \"reasoning\": \"<what you specifically saw or did not see>\" },\n \"inconsistencies\": [\"<specific finding 1>\", \"<specific finding 2>\"],\n \"context_clues\": [\"<visible text, date, tech, or era clue>\"],\n \"agrees_with_classifiers\": <true or false>,\n \"disagreement_reason\": \"<if false, explain why>\",\n \"gemini_verdict\": \"<REAL | DEEPFAKE | AI_GENERATED | SUSPICIOUS | INCONCLUSIVE>\",\n \"confidence\": <0-100>,\n \"summary\": \"<2-3 sentence forensic conclusion in plain English>\"\n}`;\n\nreturn [{\n json: {\n base64, mimeType, fileName, caption, claimDate,\n exif, exifAgeWarning, exifInsight, currentYear,\n deepfakeScore, aiGenScore,\n earliestYear, topSources, totalResults,\n geminiPrompt: prompt\n }\n}];"
},
"id": "d13daa8d-0459-4861-bd1d-e8221d074e20",
"name": "\ud83e\udde0 Prepare Gemini Context",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-1660,
-80
]
},
{
"parameters": {
"method": "POST",
"url": "=https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent?key=Your Gemini API Key",
"sendBody": true,
"contentType": "raw",
"rawContentType": "application/json",
"body": "={\n \"contents\": [{\n \"parts\": [\n { \"inline_data\": { \"mime_type\": \"{{ $json.mimeType }}\", \"data\": \"{{ $json.base64 }}\" } },\n { \"text\": {{ JSON.stringify($json.geminiPrompt) }} }\n ]\n }]\n}",
"options": {}
},
"id": "7a174066-dfb2-4fd0-88a0-76a34ee0b804",
"name": "\u2728 Gemini \u2014 Forensic Analysis",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-1440,
-80
]
},
{
"parameters": {
"jsCode": "const geminiRaw = $input.item.json;\nconst prev = $('\ud83e\udde0 Prepare Gemini Context').first().json;\n\nconst { deepfakeScore, aiGenScore, earliestYear, topSources, totalResults, exif, exifAgeWarning, currentYear, claimDate, caption, fileName, exifInsight } = prev;\n\n// \u2500\u2500 Parse Gemini JSON \u2500\u2500\nconst gText = geminiRaw.candidates?.[0]?.content?.parts?.[0]?.text || '';\nlet g;\ntry {\n g = JSON.parse(gText.replace(/```json|```/g,'').trim());\n} catch(e) {\n g = {\n deepfake_visual: { score:50, reasoning:'Gemini parse failed \u2014 using classifier scores only.' },\n ai_gen_visual: { score:50, reasoning:'Gemini parse failed \u2014 using classifier scores only.' },\n inconsistencies: ['Gemini analysis unavailable. Manual review required.'],\n context_clues: [],\n agrees_with_classifiers: true,\n disagreement_reason: '',\n gemini_verdict: 'INCONCLUSIVE',\n confidence: 20,\n summary: 'Automated forensic analysis was inconclusive. Manual review required.'\n };\n}\n\n// \u2500\u2500 Combine HF + Gemini scores (50/50) \u2500\u2500\nlet combinedDeepfake = deepfakeScore >= 0\n ? Math.round((deepfakeScore * 0.5) + (g.deepfake_visual.score * 0.5))\n : g.deepfake_visual.score;\n\nlet combinedAiGen = aiGenScore >= 0\n ? Math.round((aiGenScore * 0.5) + (g.ai_gen_visual.score * 0.5))\n : g.ai_gen_visual.score;\n\n// EXIF hard override\nif (exif.software) combinedAiGen = Math.max(combinedAiGen, 92);\n\n// \u2500\u2500 Timeliness Score \u2500\u2500\nlet timelyScore = 70;\nlet timelyReasoning = 'No reverse search results to evaluate timeline.';\nlet serpVerdict = 'NO_DATA';\n\nlet claimYear = null;\nif (claimDate) { const m=claimDate.match(/\\b(19|20)\\d{2}\\b/); if(m) claimYear=parseInt(m[0]); }\n\nif (earliestYear) {\n const ageYears = currentYear - earliestYear;\n if (claimYear) {\n const diff = claimYear - earliestYear;\n if (Math.abs(diff)<=1) { timelyScore=92; serpVerdict='CLAIM_MATCHES'; timelyReasoning=`\u2705 Claimed (${claimYear}) matches earliest found (${earliestYear}).`; }\n else if (diff>1) { timelyScore=5; serpVerdict='CLAIM_MISMATCH'; timelyReasoning=`\ud83d\udea8 MISMATCH: Claimed ${claimYear} but image first appeared online in ${earliestYear} \u2014 ${diff} years earlier.`; }\n else { timelyScore=60; serpVerdict='MINOR_MISMATCH'; timelyReasoning=`Minor gap: claimed ${claimYear}, earliest found ${earliestYear}.`; }\n } else {\n if (ageYears>=3) { timelyScore=12; serpVerdict='LIKELY_OLD'; timelyReasoning=`Image from ${earliestYear} \u2014 ${ageYears} years ago. HIGH risk of reshare.`; }\n else if (ageYears>=1) { timelyScore=48; serpVerdict='POSSIBLY_OLD'; timelyReasoning=`First appeared ~${earliestYear}. May be reshared.`; }\n else { timelyScore=88; serpVerdict='LIKELY_RECENT'; timelyReasoning=`Appears recent (first found ${earliestYear}).`; }\n }\n timelyReasoning += ` Found ${totalResults} total appearances.`;\n} else if (totalResults>0) {\n timelyScore=60; serpVerdict='UNVERIFIED'; timelyReasoning=`Found ${totalResults} appearances but no date determined.`;\n} else {\n timelyScore=70; serpVerdict='NO_PRIOR_RECORD'; timelyReasoning='No prior appearances found. Image may be new or not indexed.';\n}\n\nif (exifAgeWarning) { timelyScore=Math.min(timelyScore,18); timelyReasoning=exifAgeWarning+' '+timelyReasoning; }\n\n// \u2500\u2500 Formula \u2500\u2500\nconst ai_real = 100 - combinedAiGen;\nconst no_deepfake = 100 - combinedDeepfake;\nconst timely = timelyScore;\nconst overall = Math.round((ai_real*0.35)+(no_deepfake*0.35)+(timely*0.30));\nconst flagged = overall < 75;\n\n// \u2500\u2500 Final Verdict \u2500\u2500\nlet finalVerdict, alertLevel, verdictEmoji;\nif (exif.software) { finalVerdict='AI GENERATED \u2014 CONFIRMED BY METADATA'; verdictEmoji='\ud83e\udd16'; alertLevel='HIGH'; }\nelse if (combinedAiGen>=80) { finalVerdict='AI GENERATED'; verdictEmoji='\ud83e\udd16'; alertLevel='HIGH'; }\nelse if (combinedDeepfake>=78) { finalVerdict='DEEPFAKE DETECTED'; verdictEmoji='\ud83d\udc64'; alertLevel='HIGH'; }\nelse if (serpVerdict==='CLAIM_MISMATCH'||serpVerdict==='LIKELY_OLD') { finalVerdict='MISCLAIMED / OLD PHOTO'; verdictEmoji='\u23f3'; alertLevel='HIGH'; }\nelse if (overall<40) { finalVerdict='ALMOST CERTAINLY FAKE'; verdictEmoji='\ud83d\udd34'; alertLevel='HIGH'; }\nelse if (overall<75) { finalVerdict='SUSPICIOUS \u2014 NEEDS REVIEW'; verdictEmoji='\u26a0\ufe0f'; alertLevel='MEDIUM'; }\nelse if (overall>=85) { finalVerdict='LIKELY REAL & TIMELY'; verdictEmoji='\u2705'; alertLevel='LOW'; }\nelse { finalVerdict='PROBABLY REAL'; verdictEmoji='\u2705'; alertLevel='LOW'; }\n\n// \u2500\u2500 Red Flags \u2500\u2500\nconst redFlags = [\n exif.software && `\ud83e\udd16 EXIF contains AI software: \"${exif.software}\"`,\n exifAgeWarning && `\ud83d\udcc5 ${exifAgeWarning}`,\n exif.wasEdited && `\u270f\ufe0f File modified after creation (${exif.modifyDate})`,\n !exif.hasExif && `\ud83d\uddc2\ufe0f No EXIF \u2014 screenshot or stripped`,\n serpVerdict==='CLAIM_MISMATCH' && `\ud83d\udea8 Claimed ${claimYear} \u2260 earliest found ${earliestYear}`,\n serpVerdict==='LIKELY_OLD' && `\u23f3 Image from ${earliestYear} \u2014 ${currentYear-earliestYear} years ago`,\n combinedDeepfake>=75 && `\ud83d\udc64 Deepfake: ${combinedDeepfake}% (HF:${deepfakeScore}% | Gemini:${g.deepfake_visual.score}%)`,\n combinedAiGen>=75 && `\ud83e\udd16 AI-gen: ${combinedAiGen}% (HF:${aiGenScore}% | Gemini:${g.ai_gen_visual.score}%)`,\n !g.agrees_with_classifiers && `\u26a1 Gemini disagrees: ${g.disagreement_reason}`\n].filter(Boolean);\n\nreturn [{\n json: {\n fileName, caption, claimDate,\n finalVerdict, verdictEmoji, alertLevel,\n formula: { ai_real, no_deepfake, timely, overall, flagged, calculation:`(${ai_real}\u00d70.35)+(${no_deepfake}\u00d70.35)+(${timely}\u00d70.30)=${overall}%` },\n scores: {\n deepfake: { hf:deepfakeScore, gemini:g.deepfake_visual.score, combined:combinedDeepfake },\n ai_generated: { hf:aiGenScore, gemini:g.ai_gen_visual.score, combined:combinedAiGen },\n timely: timelyScore\n },\n reasoning: {\n deepfake: g.deepfake_visual.reasoning,\n ai_generated: g.ai_gen_visual.reasoning,\n timeline: timelyReasoning,\n gemini_summary: g.summary\n },\n redFlags,\n inconsistencies: g.inconsistencies || [],\n contextClues: g.context_clues || [],\n geminiVerdict: g.gemini_verdict,\n geminiConfidence: g.confidence,\n agreesWithClassifiers: g.agrees_with_classifiers,\n disagreementReason: g.disagreement_reason || null,\n reverseSearch: { totalResults, earliestYear, serpVerdict, topSources },\n exif: {\n hasExif: exif.hasExif, fileType: exif.fileType,\n dateTaken: exif.dateTimeOriginal, modifyDate: exif.modifyDate,\n wasEdited: exif.wasEdited, camera: exif.make,\n software: exif.software, gpsFound: exif.gpsFound,\n insight: exifInsight\n },\n analyzedAt: new Date().toISOString()\n }\n}];"
},
"id": "23400f51-649d-44a8-b13e-c1ceff317cd2",
"name": "\u2696\ufe0f Verdict Engine",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-1220,
-80
]
},
{
"parameters": {
"jsCode": "const d = $input.item.json;\n\nconst P = {\n HIGH: { bg:'#fff5f5', border:'#fc8181', text:'#c53030', dot:'#e53e3e' },\n MEDIUM: { bg:'#fffbeb', border:'#f6ad55', text:'#c05621', dot:'#dd6b20' },\n LOW: { bg:'#f0fff4', border:'#68d391', text:'#276749', dot:'#38a169' }\n};\nconst p = P[d.alertLevel] || P.MEDIUM;\n\n// Score dot indicator\nconst dot = (score) => score >= 70 ? '\ud83d\udd34' : score >= 40 ? '\ud83d\udfe1' : '\ud83d\udfe2';\n\n// Score row \u2014 clean single line\nconst scoreRow = (label, score) => `\n <tr>\n <td style=\"padding:10px 16px;font-size:15px;color:#2d3748;font-weight:600\">${label}</td>\n <td style=\"padding:10px 16px;text-align:right\">\n <span style=\"font-size:15px;font-weight:800;color:#2d3748\">${score}%</span>\n ${dot(score)}\n </td>\n </tr>`;\n\n// Red flag row\nconst flagRow = (flag) => `\n <tr>\n <td style=\"padding:6px 0;font-size:14px;color:#c53030\">\n ${flag}\n </td>\n </tr>`;\n\nconst now = new Date(d.analyzedAt);\nconst dateStr = now.toLocaleDateString('en-US', { weekday:'long', year:'numeric', month:'long', day:'numeric' });\n\n// EXIF one-liner\nconst exifLine = d.exif.software\n ? `\ud83d\udea8 AI software detected in metadata: \"${d.exif.software}\"`\n : d.exif.dateTaken\n ? `\ud83d\udcf7 Taken: ${d.exif.dateTaken}${d.exif.camera ? ' \u00b7 ' + d.exif.camera : ''}${d.exif.wasEdited ? ' \u00b7 \u26a0\ufe0f Edited after creation' : ''}`\n : d.exif.hasExif\n ? `\ud83d\udcf7 EXIF found \u00b7 No date recorded`\n : `\ud83d\udcf7 No EXIF data \u00b7 Likely screenshot or downloaded`;\n\n// Reverse search one-liner\nconst serpLine = d.reverseSearch.earliestYear\n ? `\ud83d\udd0d Found ${d.reverseSearch.totalResults} prior appearance(s) \u00b7 Earliest: ${d.reverseSearch.earliestYear}`\n : d.reverseSearch.totalResults > 0\n ? `\ud83d\udd0d Found ${d.reverseSearch.totalResults} prior appearance(s) \u00b7 No date determined`\n : `\ud83d\udd0d No prior appearances found online`;\n\n// Claim block (only if provided)\nconst claimLine = (d.caption || d.claimDate)\n ? `<div style=\"background:#ebf8ff;border-left:4px solid #63b3ed;padding:12px 16px;margin-bottom:20px;border-radius:0 8px 8px 0\">\n <p style=\"font-size:12px;font-weight:700;color:#2b6cb0;margin-bottom:4px;text-transform:uppercase;letter-spacing:0.5px\">Submitted Claim</p>\n ${d.caption ? `<p style=\"font-size:14px;color:#2c5282;margin-bottom:2px\">\ud83d\udcdd ${d.caption}</p>` : ''}\n ${d.claimDate ? `<p style=\"font-size:14px;color:#2c5282\">\ud83d\udcc5 Claimed date: ${d.claimDate}</p>` : ''}\n </div>`\n : '';\n\nconst html = `<!DOCTYPE html>\n<html>\n<head>\n <meta charset=\"UTF-8\">\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n <style>\n * { box-sizing:border-box; margin:0; padding:0; }\n body { font-family: 'Segoe UI', Arial, sans-serif; background: #f0f2f5; }\n .wrap { max-width: 560px; margin: 24px auto; }\n table { width:100%; border-collapse:collapse; }\n </style>\n</head>\n<body>\n<div class=\"wrap\">\n\n <!-- HEADER -->\n <div style=\"background:linear-gradient(135deg,#0f0c29,#302b63);padding:28px 32px;border-radius:12px 12px 0 0\">\n <p style=\"color:#a0aec0;font-size:11px;letter-spacing:2px;text-transform:uppercase;margin-bottom:6px\">Powered by</p>\n <h1 style=\"color:#ffffff;font-size:26px;font-weight:900;letter-spacing:-0.5px\">\u0938\u0924\u094d\u092f\u0924\u093e</h1>\n <p style=\"color:#a0aec0;font-size:12px;margin-top:6px\">${dateStr} \u00b7 ${d.fileName}</p>\n </div>\n\n <!-- VERDICT -->\n <div style=\"background:${p.bg};border:2px solid ${p.border};padding:22px 32px;margin-top:2px\">\n <h2 style=\"color:${p.text};font-size:22px;font-weight:900\">${d.verdictEmoji} ${d.finalVerdict}</h2>\n <div style=\"display:flex;align-items:center;gap:16px;margin-top:14px\">\n <div style=\"text-align:center\">\n <div style=\"font-size:42px;font-weight:900;color:${p.dot};line-height:1\">${d.formula.overall}<span style=\"font-size:18px\">%</span></div>\n <div style=\"font-size:11px;color:${p.text};margin-top:4px;font-weight:600\">TRUST SCORE ${d.formula.flagged ? '\ud83d\udea9' : '\u2705'}</div>\n </div>\n <div style=\"flex:1;border-left:2px solid ${p.border};padding-left:16px\">\n <p style=\"font-size:13px;color:${p.text};line-height:1.6\">${d.reasoning.gemini_summary}</p>\n </div>\n </div>\n </div>\n\n <!-- BODY -->\n <div style=\"background:#ffffff;padding:24px 32px\">\n\n ${claimLine}\n\n <!-- SCORES -->\n <p style=\"font-size:11px;font-weight:700;color:#a0aec0;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px\">Scores</p>\n <div style=\"border:1px solid #edf2f7;border-radius:8px;overflow:hidden;margin-bottom:20px\">\n <table>\n ${scoreRow('\ud83d\udc64 Deepfake', d.scores.deepfake.combined)}\n <tr><td colspan=\"2\" style=\"height:1px;background:#edf2f7;padding:0\"></td></tr>\n ${scoreRow('\ud83e\udd16 AI Generated', d.scores.ai_generated.combined)}\n <tr><td colspan=\"2\" style=\"height:1px;background:#edf2f7;padding:0\"></td></tr>\n ${scoreRow('\u23f3 Timely / Context', d.scores.timely)}\n </table>\n </div>\n\n <!-- RED FLAGS -->\n ${d.redFlags.length > 0 ? `\n <p style=\"font-size:11px;font-weight:700;color:#a0aec0;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px\">Red Flags</p>\n <div style=\"border:1px solid #fc8181;border-radius:8px;padding:4px 16px;background:#fff5f5;margin-bottom:20px\">\n <table>\n ${d.redFlags.map(f => flagRow(f)).join('')}\n </table>\n </div>` : `\n <div style=\"border:1px solid #68d391;border-radius:8px;padding:12px 16px;background:#f0fff4;margin-bottom:20px\">\n <p style=\"font-size:14px;color:#276749\">\u2705 No red flags detected</p>\n </div>`}\n\n <!-- EVIDENCE -->\n <p style=\"font-size:11px;font-weight:700;color:#a0aec0;text-transform:uppercase;letter-spacing:1px;margin-bottom:8px\">Evidence</p>\n <div style=\"border:1px solid #edf2f7;border-radius:8px;padding:12px 16px;margin-bottom:6px;background:#f7fafc\">\n <p style=\"font-size:14px;color:#4a5568\">${exifLine}</p>\n </div>\n <div style=\"border:1px solid #edf2f7;border-radius:8px;padding:12px 16px;background:#f7fafc;margin-bottom:20px\">\n <p style=\"font-size:14px;color:#4a5568\">${serpLine}</p>\n </div>\n\n <!-- DISCLAIMER -->\n <p style=\"font-size:12px;color:#a0aec0;line-height:1.6;border-top:1px solid #edf2f7;padding-top:16px\">\n \u26a0\ufe0f Automated analysis. Not 100% accurate \u2014 always verify manually for critical decisions.\n </p>\n\n </div>\n\n <!-- FOOTER -->\n<div style=\"background:#f7fafc;padding:16px 32px;border-radius:0 0 12px 12px;border-top:1px solid #edf2f7\">\n <p style=\"font-size:14px;font-weight:900;color:#2d3748;margin-bottom:4px\">\u0938\u0924\u094d\u092f\u0924\u093e</p>\n <p style=\"font-size:11px;color:#a0aec0\">Powered by Wvolf \u00b7 dima806 \u00b7 Gemini \u00b7 SerpAPI \u00b7 EXIF</p>\n</div>\n</div>\n</body>\n</html>`;\n\nreturn [{\n json: {\n html,\n subject: `${d.verdictEmoji} \u0938\u0924\u094d\u092f\u0924\u093e: ${d.finalVerdict} [${d.formula.overall}%] \u2014 ${d.fileName}`,\n postmanPayload: {\n status: 'success',\n file: d.fileName,\n claim: { caption: d.caption, claimed_date: d.claimDate },\n verdict: { label: d.finalVerdict, emoji: d.verdictEmoji, alert_level: d.alertLevel, overall_trust_score: d.formula.overall },\n scores: {\n deepfake: { hf: d.scores.deepfake.hf, gemini: d.scores.deepfake.gemini, combined: d.scores.deepfake.combined },\n ai_generated: { hf: d.scores.ai_generated.hf, gemini: d.scores.ai_generated.gemini, combined: d.scores.ai_generated.combined },\n timeliness: d.scores.timely\n },\n formula: d.formula,\n red_flags: d.redFlags,\n evidence: {\n exif: d.exif,\n reverse_search: d.reverseSearch,\n gemini_reasoning: d.reasoning,\n gemini_verdict: d.geminiVerdict,\n gemini_confidence: d.geminiConfidence,\n inconsistencies: d.inconsistencies,\n context_clues: d.contextClues\n },\n analyzed_at: d.analyzedAt\n }\n }\n}];"
},
"id": "38cd9456-034a-48c1-aca8-05d3da219087",
"name": "\ud83c\udfa8 Build Email + JSON",
"type": "n8n-nodes-base.code",
"typeVersion": 2,
"position": [
-1000,
-80
]
},
{
"parameters": {
"method": "POST",
"url": "https://api.resend.com/emails",
"sendHeaders": true,
"headerParameters": {
"parameters": [
{
"name": "Authorization",
"value": "Bearer Your Resend Key"
},
{
"name": "Content-Type",
"value": "application/json"
}
]
},
"sendBody": true,
"contentType": "raw",
"rawContentType": "application/json",
"body": "={\n \"from\": \"Detector <onboarding@resend.dev>\",\n \"to\": [\"addyour@email.com\"],\n \"subject\": {{ JSON.stringify($json.subject) }},\n \"html\": {{ JSON.stringify($json.html) }}\n}",
"options": {}
},
"id": "3bc4bff3-574b-4b08-83bf-e96736184173",
"name": "\ud83d\udce7 Send via Resend",
"type": "n8n-nodes-base.httpRequest",
"typeVersion": 4.2,
"position": [
-780,
-180
]
},
{
"parameters": {
"respondWith": "json",
"responseBody": "={{ JSON.stringify($('\ud83c\udfa8 Build Email + JSON').item.json.postmanPayload) }}",
"options": {}
},
"id": "e677ea70-f896-4fa9-af03-53cf47dfe153",
"name": "\u2705 Respond to Postman",
"type": "n8n-nodes-base.respondToWebhook",
"typeVersion": 1,
"position": [
-780,
20
]
}
],
"connections": {
"\ud83d\udce5 Receive Image + Claims": {
"main": [
[
{
"node": "\ud83d\udce6 Extract Base64 + EXIF",
"type": "main",
"index": 0
}
]
]
},
"\ud83d\udce6 Extract Base64 + EXIF": {
"main": [
[
{
"node": "\ud83e\udd16 Wvolf \u2014 Deepfake",
"type": "main",
"index": 0
}
]
]
},
"\ud83e\udd16 Wvolf \u2014 Deepfake": {
"main": [
[
{
"node": "\ud83d\udcbe Save Wvolf Score",
"type": "main",
"index": 0
}
]
]
},
"\ud83d\udcbe Save Wvolf Score": {
"main": [
[
{
"node": "\ud83d\uddbc\ufe0f dima806 \u2014 AI Gen",
"type": "main",
"index": 0
}
]
]
},
"\ud83d\uddbc\ufe0f dima806 \u2014 AI Gen": {
"main": [
[
{
"node": "\ud83d\udcbe Save dima806 Score",
"type": "main",
"index": 0
}
]
]
},
"\ud83d\udcbe Save dima806 Score": {
"main": [
[
{
"node": "\ud83c\udf10 imgbb \u2014 Public URL",
"type": "main",
"index": 0
}
]
]
},
"\ud83c\udf10 imgbb \u2014 Public URL": {
"main": [
[
{
"node": "\ud83d\udcbe Save imgbb URL",
"type": "main",
"index": 0
}
]
]
},
"\ud83d\udcbe Save imgbb URL": {
"main": [
[
{
"node": "\ud83d\udd0d SerpAPI \u2014 Reverse Search",
"type": "main",
"index": 0
}
]
]
},
"\ud83d\udd0d SerpAPI \u2014 Reverse Search": {
"main": [
[
{
"node": "\ud83e\udde0 Prepare Gemini Context",
"type": "main",
"index": 0
}
]
]
},
"\ud83e\udde0 Prepare Gemini Context": {
"main": [
[
{
"node": "\u2728 Gemini \u2014 Forensic Analysis",
"type": "main",
"index": 0
}
]
]
},
"\u2728 Gemini \u2014 Forensic Analysis": {
"main": [
[
{
"node": "\u2696\ufe0f Verdict Engine",
"type": "main",
"index": 0
}
]
]
},
"\u2696\ufe0f Verdict Engine": {
"main": [
[
{
"node": "\ud83c\udfa8 Build Email + JSON",
"type": "main",
"index": 0
}
]
]
},
"\ud83c\udfa8 Build Email + JSON": {
"main": [
[
{
"node": "\ud83d\udce7 Send via Resend",
"type": "main",
"index": 0
},
{
"node": "\u2705 Respond to Postman",
"type": "main",
"index": 0
}
]
]
}
},
"active": false,
"settings": {
"executionOrder": "v1"
},
"versionId": "1255708f-635e-4cf5-a673-2e04c19bda5b",
"id": "s9rpHNhStSMPamgU",
"tags": []
}
Pro
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About this workflow
fakey. Uses httpRequest. Webhook trigger; 15 nodes.
Source: https://github.com/PratikPawar020403/automation-workflows/blob/6782e0ed6b6f59a8811d23cb4f7e5768629812e1/automations/fakey.json — original creator credit. Request a take-down →
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