Image spam floods platforms with promotional content, scam imagery, and repetitive uploads that frustrate users and degrade community quality. Our AI detects spam patterns across visual content, text-in-images, and upload behavior to keep your platform clean.
Try Free DemoImage spam might seem like a minor nuisance compared to explicit or violent content, but its impact on platforms is substantial. Studies show that spam is the number one reason users cite for leaving online communities. Every promotional post, scam listing, or repetitive upload erodes user trust and engagement.
Beyond user experience, spam creates operational costs. Support teams field complaints, moderation queues fill with repetitive reports, and engineering resources are diverted to whack-a-mole spam fighting. For marketplaces and classified sites, spam listings directly undermine trust and transaction volumes.
Modern image spam is sophisticated. Spammers use slight image variations to evade hash-based detection, embed promotional text in images to bypass text filters, and coordinate networks of fake accounts for volume attacks. Effective spam fighting requires equally sophisticated detection.
Our spam detection analyzes multiple signals to identify spam content even when individual signals wouldn't trigger detection:
Identify near-duplicate images and variations that spammers use to evade exact-match filters. Catches resized, filtered, or slightly modified spam images.
OCR extracts text from images and identifies promotional language, phone numbers, URLs, and other spam indicators in 50+ languages.
Detect common scam imagery patterns: too-good-to-be-true offers, fake luxury goods, counterfeit products, and impersonation attempts.
Identify stock photo watermarks, competitor logos, and other indicators of unauthorized or spammy image usage.
Detect QR codes in images that often link to spam sites, phishing pages, or external promotional content.
Combine image analysis with upload patterns, account age, and other signals to identify coordinated spam campaigns.
# Python - Detect spam images import requests def check_for_spam(image_url, api_key, user_context=None): response = requests.post( "https://api.imagemoderationapi.com/v1/moderate", headers={"Authorization": f"Bearer {api_key}"}, json={ "image_url": image_url, "models": ["spam", "ocr", "qr"], "context": user_context # Optional: account age, post count, etc. } ) result = response.json() # Check for spam indicators spam_score = result["moderation_classes"]["spam"]["score"] has_promo_text = result.get("ocr_analysis", {}).get("promotional", False) has_qr = result.get("qr_detected", False) if spam_score > 0.9 or (spam_score > 0.7 and has_promo_text): return {"action": "block", "reason": "spam_detected"} if has_qr or spam_score > 0.5: return {"action": "review", "reason": "possible_spam"} return {"action": "allow"}
Our models distinguish between spam and legitimate business content by analyzing multiple signals including image quality, text patterns, and context. Verified business accounts can have adjusted thresholds. We provide confidence scores so you can tune sensitivity for different content areas.
We use perceptual hashing and deep learning that catches image variations, not just exact matches. Our models detect spam patterns even when images are modified, text is repositioned, or colors are changed. We continuously update models as new spam techniques emerge.
You can pass optional context like account age, post frequency, and profile completeness. A borderline image from a new account with rapid-fire posting patterns is more likely spam than the same image from an established user.
We have specialized models for marketplace spam including counterfeit detection, scam listing patterns, and misleading product imagery. These models understand the specific patterns that affect classified and e-commerce platforms.
Keep your platform clean with AI-powered spam detection. Start your free trial today.
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