Harness the power of deep learning computer vision to detect NSFW imagery, violence, weapons, deepfakes, drug paraphernalia, and manipulated media with pixel-level precision and sub-200ms response times. Our models analyze every frame, every pixel, and every context.
Our visual content analysis platform leverages convolutional neural networks, transformer architectures, and multi-scale feature extraction to understand images and video at a depth that surpasses traditional rule-based systems by orders of magnitude.
Classify explicit, suggestive, and adult content with nuanced understanding of context. Distinguish between artistic nudity, medical imagery, and policy-violating material with 99.5% precision across diverse cultural contexts.
Identify graphic violence, gore, physical altercations, self-harm imagery, and threatening scenarios. Our models recognize both overt and subtle depictions of violence including blood, injuries, and aggressive postures.
Detect firearms, knives, explosives, and other dangerous objects even when partially obscured, modified in appearance, or presented in unusual orientations. Context-aware analysis distinguishes toys from real threats.
Recognize drug-related content including paraphernalia, illicit substances, and promotional material. Trained on comprehensive datasets of controlled substances and associated equipment across global variations.
Identify AI-generated and manipulated imagery using artifact analysis, frequency domain inspection, and GAN-signature detection. Catches face swaps, body manipulations, and synthetically generated content.
Extract and analyze text embedded within images including memes, screenshots, handwritten notes, overlay text, and signs. Supports 100+ languages and feeds extracted text into NLP analysis pipelines.
At the heart of visual content analysis lies robust object detection. Our system identifies and localizes over 50,000 object categories within images using state-of-the-art region proposal networks and anchor-free detection architectures. Each detected object receives a bounding box with pixel-accurate coordinates and a hierarchical classification label.
The classification pipeline extends well beyond simple labeling. Every detected object is evaluated for contextual relevance, potential policy implications, and its relationship to surrounding objects. A kitchen knife in a cooking tutorial carries a fundamentally different risk profile than the same knife in a threatening pose, and our models understand this distinction.
Key capabilities include: multi-object tracking across video frames, occlusion handling for partially hidden objects, scale-invariant detection from thumbnails to high-resolution imagery, and real-time batch processing of thousands of images per second.
Every image processed through our visual analysis pipeline receives a comprehensive set of classification labels, each paired with a calibrated confidence score. These scores reflect the true probability of each category applying to the content, enabling platform operators to set precise thresholds that balance safety with user freedom.
Our classification taxonomy covers hundreds of content categories organized in a hierarchical structure. At the top level, broad categories like NSFW, Violence, Hate Symbols, and Drug Content branch into dozens of subcategories, giving moderators granular control over enforcement decisions. Each category threshold is independently configurable through the API.
Advanced scoring features: Bayesian confidence calibration, ensemble model agreement metrics, uncertainty quantification for edge cases, and severity gradients that distinguish between mildly suggestive and explicitly graphic content.
Our visual content analysis engine is built on a foundation of deep convolutional neural networks that have been specifically architected and trained for content moderation tasks. Unlike general-purpose image classification models, our networks are optimized to detect the subtle visual patterns associated with policy-violating content while maintaining extremely low false positive rates on legitimate imagery. The backbone architecture uses a modified EfficientNet-V2 design with custom attention modules that focus computational resources on the most safety-relevant regions of each image.
The initial feature extraction stage processes raw image pixels through a series of convolutional layers that progressively build more abstract representations. Early layers detect edges, textures, and color gradients, while deeper layers assemble these primitives into complex object representations, scene compositions, and semantic understanding of the visual content. This hierarchical approach mirrors the human visual cortex and enables the system to simultaneously analyze fine-grained pixel details and broad compositional context.
Content that violates platform policies can appear at any scale within an image. A hate symbol might occupy a small corner of an otherwise innocuous photograph, or violent imagery might be subtly embedded within a complex scene. Our feature pyramid network processes images at multiple resolutions simultaneously, building a multi-scale representation that ensures detection capability regardless of object size or image resolution. The pyramid spans from fine-grained 4x4 pixel feature maps that capture tiny details to coarse 128x128 maps that understand global scene composition.
Pixel-level analysis goes beyond object detection to provide semantic segmentation of entire images. Every pixel is classified into semantic categories, creating detailed maps that show exactly which regions of an image contain concerning content. This granular analysis supports advanced moderation actions such as selective blurring of specific image regions, precise bounding box annotations for human reviewers, and detailed audit trails that document exactly what the system detected and where.
The proliferation of image editing tools and generative AI models has made it increasingly easy to create manipulated or entirely synthetic visual content. Our deepfake detection system employs multiple complementary analysis techniques to identify altered imagery. Frequency domain analysis examines the spectral characteristics of images, detecting the telltale artifacts left by GAN-based generation and face-swapping algorithms. Compression artifact analysis identifies inconsistencies in JPEG quantization tables that reveal splicing and compositing operations.
GAN-generated content identification is a particularly critical capability as AI image generators become more sophisticated. Our detection models are trained on outputs from all major generative architectures including diffusion models, variational autoencoders, and adversarial networks. The system analyzes statistical properties at both the pixel and feature level, identifying the subtle patterns that distinguish synthetic content from genuine photographs. Regular retraining against the latest generation models ensures detection stays ahead of advancing synthesis capabilities. These same techniques also catch less sophisticated manipulations such as copy-move forgeries, image splicing, metadata tampering, and unauthorized resizing or cropping intended to alter context.
Visual content analysis extends to intellectual property protection through comprehensive logo and brand detection. Our system identifies over 500,000 registered logos, trademarks, and brand marks across products, packaging, signage, and digital overlays. This capability helps marketplaces detect counterfeit product listings, protects brands from unauthorized use in user-generated content, and supports advertising compliance by identifying unauthorized brand placements in sponsored content.
The brand detection engine uses metric learning to understand visual similarity at a semantic level, catching modified, distorted, or partially obscured logos that simpler template-matching approaches would miss. This is essential for identifying counterfeit goods where sellers intentionally alter brand marks to evade detection while still leveraging the brand's visual identity to deceive buyers.
Platforms serving diverse audiences require content filtering that adapts to the viewer's age group. Our age-appropriate content filtering system classifies visual content across multiple maturity tiers, from content suitable for all ages to material restricted to verified adults. The system evaluates not just explicit content categories but also considers thematic elements, emotional intensity, and contextual factors that affect age appropriateness. Horror imagery, intense action sequences, suggestive poses, and substance depictions are all evaluated against age-tier thresholds to provide comprehensive audience protection.
Facial age estimation capabilities contribute to child safety by identifying when minors may appear in user-submitted content. When combined with content classification, this enables platforms to apply enhanced protection for content involving younger individuals, automatically flagging potentially exploitative imagery for priority human review. These protections operate within strict privacy frameworks, with facial analysis data processed transiently and never stored beyond the moderation decision window.
From the moment an image enters our API to the final moderation decision, every step is optimized for speed and accuracy. The pipeline begins with format normalization and intelligent preprocessing, automatically handling JPEG, PNG, WebP, HEIC, GIF, and TIFF formats. Metadata extraction captures EXIF data, GPS coordinates, and camera information for provenance analysis.
The normalized image flows through parallel analysis tracks: object detection, scene classification, text extraction (OCR), face analysis, manipulation detection, and content-specific classifiers. Results from each track are fused through our proprietary ensemble scoring engine which weighs the outputs according to their individual confidence levels and cross-validates findings across tracks.
Pipeline stages: Format normalization, metadata extraction, multi-scale feature extraction, parallel classifier execution, ensemble fusion, confidence calibration, threshold evaluation, and structured result delivery. The entire pipeline completes in under 200 milliseconds for standard images.
Our visual analysis models are built on a multi-branch neural architecture where specialized sub-networks handle different aspects of content analysis. The shared backbone extracts general visual features, while dedicated classification heads focus on specific content categories. This design achieves both computational efficiency and detection specialization.
Training employs a curriculum learning strategy where models first learn on clear-cut examples before progressively encountering more ambiguous and challenging cases. This mirrors how human moderators develop expertise and produces models that are robust to borderline content. Active learning pipelines continuously identify the most informative examples from production traffic for human annotation, creating a feedback loop that drives perpetual accuracy improvement.
Training infrastructure: Distributed training across thousands of GPUs, dataset of 500M+ annotated images, adversarial training for robustness, multi-task learning across 200+ categories, weekly model refresh cycles, and A/B testing for every production update.
A significant portion of harmful content on digital platforms takes the form of text embedded within images. Hate speech memes, threatening messages overlaid on photographs, misinformation presented as infographics, and policy-violating offers displayed in product images all require the ability to read and understand text within visual content. Our OCR engine extracts text from images with over 98% character-level accuracy across 100+ languages, including right-to-left scripts, ideographic writing systems, and mixed-language content.
The extracted text is automatically routed through our natural language processing pipeline for toxicity analysis, hate speech detection, and policy compliance checking. This creates a seamless bridge between visual and textual content moderation, ensuring that harmful messages cannot evade detection simply by being rendered as images rather than typed as text. The system handles challenging scenarios including stylized fonts, curved text paths, low contrast text, text partially obscured by background imagery, and intentionally obfuscated characters used by bad actors to avoid detection.
Beyond individual object and text detection, our system performs holistic scene understanding that considers the complete visual narrative of an image. Scene classification identifies the setting and environment, whether a beach, classroom, protest, medical procedure, or nightlife venue. This contextual understanding fundamentally changes how individual elements within the image are interpreted. The same level of undress appropriate in a swimwear advertisement becomes a policy violation when the setting suggests a non-consensual context.
Activity recognition within scenes identifies what is happening in the image, not just what objects are present. The system understands the difference between a martial arts demonstration and a street fight, between surgical imagery and violent injury, and between a museum exhibit of historical weapons and a threatening weapons display. This activity-level understanding is powered by spatio-temporal reasoning models that consider the spatial relationships between detected objects and the implied dynamics of the scene.
Video content presents unique challenges for visual analysis because harmful content may appear only briefly within an otherwise benign video, or the harmful nature of content may only become apparent through the temporal progression of scenes. Our video analysis system processes key frames at adaptive intervals, increasing frame sampling density when content risk indicators are detected and reducing it during clearly benign segments. This intelligent sampling achieves comprehensive coverage while maintaining real-time processing speeds.
Temporal reasoning tracks objects, people, and activities across frames to understand how scenes develop over time. This capability catches content that transitions from benign to harmful, identifies brief flashes of policy-violating content intended to slip past per-frame analysis, and provides complete context for moderation decisions on video content. The system generates timestamped annotations that enable precise content removal or age-gating of specific video segments rather than requiring takedown of entire videos.
Our visual content analysis covers every category of harmful imagery that platforms need to moderate, with specialized models trained for each threat type.
Identifies hate symbols, extremist iconography, and coded visual signals across historical and emerging symbol databases. Updated continuously as new hate symbols are cataloged by monitoring organizations and research institutions worldwide.
Detects exposed personal information in images including ID cards, credit cards, social security numbers, and license plates. Prevents accidental or malicious exposure of sensitive personally identifiable information shared through visual content.
Identifies content created by diffusion models, GANs, and other AI image generators. Analyzes statistical signatures, artifact patterns, and spectral anomalies that differentiate synthetic imagery from authentic photographs and artwork.
Detects unauthorized use of copyrighted images, watermarked content, and trademarked brand logos. Supports rights holder protection with perceptual hashing for near-duplicate detection and visual similarity matching across modified copies.
Common questions about our visual content analysis capabilities, accuracy, and integration.
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