GenAI Vision Security Checklist

Checklist for Vision Security

Adversarial Risks in Image Generation

• Adversarial Perturbation Testing: Assess if slight pixel modifications in input images can result in undesired output manipulations.

• Gradient-based Attack Resistance: Verify resistance to gradient-based attacks like FGSM (Fast Gradient Sign Method) that can subtly alter inputs to mislead model behavior.

• Detection of Adversarial Images: Implement mechanisms to detect adversarial images designed to deceive the image generation model.

• Robustness Against Style Transfer Attacks: Test if adversarially crafted style-transfer inputs can be used to manipulate generated images.

Data Poisoning and Model Integrity

• Data Augmentation Defense: Use data augmentation techniques to make the model more resilient against poisoned training data.

• Dataset Diversity Validation: Ensure that the dataset used for training is diverse and doesn’t favor specific biases that could lead to unintended outputs.

• Synthetic Data Injection Detection: Implement checks to detect if synthetic or poisoned data is being injected into the training or inference pipeline.

• Poisoned Image Detection: Regularly scan training datasets for poisoned images or datasets that could influence model behavior.

Output Integrity and Quality Control

• Watermark Resilience Testing: Test the model’s ability to embed watermarks in generated images that remain intact despite adversarial attacks.

• Content Distortion Testing: Check if generated images can be easily distorted or altered by slight changes, compromising the integrity of the output.

• Quality Consistency Checks: Implement metrics to monitor the consistency of image quality across different resolutions and outputs.

• AI Watermarking: Integrate techniques to embed invisible watermarks in generated images, helping to track the origin of the images and detect tampering.

Deepfake and Synthetic Media Security

• Deepfake Detection Integration: Implement deepfake detection tools to identify if the generated images are being used maliciously.

• Face Generation Ethics Check: Ensure that generated images, especially those involving human faces, adhere to ethical guidelines and cannot be easily manipulated for harmful purposes.

• Image Attribution Mechanisms: Use techniques like cryptographic hashing or digital signatures to attribute generated images to specific sources.

• Realism Level Limitation: Consider limiting the realism of generated images to avoid them being confused with real images (e.g., lowering resolution or adding synthetic artifacts).

Input Validation Specific to Images

• Image Size Validation: Check for oversized input images that could cause denial of service or resource exhaustion.

• Image Metadata Sanitization: Sanitize EXIF data in input images to avoid metadata-based attacks (e.g., location data leaks).

• Color Space Validation: Ensure inputs conform to expected color spaces (e.g., RGB) to prevent issues from unexpected formats.

• File Type Enforcement: Restrict allowed file types (e.g., JPEG, PNG) to prevent attacks through unusual file types (e.g., TIFF with hidden data).

Output Validation and Filtering Specific to Images

• Inappropriate Content Detection: Implement classifiers to detect nudity, violence, or other inappropriate content in generated images.

• Output Resolution Limitations: Set limits on the resolution of generated images to prevent misuse in creating ultra-high-resolution fake content.

• Image Blurring of Sensitive Areas: Automatically blur faces or sensitive areas in generated images unless specifically intended for generation.

• Generated Content Moderation: Regularly review generated content to ensure that outputs align with ethical guidelines and platform policies.

Image Processing and Storage Security

• Secure Image Storage: Ensure that generated images are stored in secure, access-controlled environments to prevent unauthorized access.

• Image Hashing for Integrity: Store hashes of generated images to detect any unauthorized modifications during storage or transmission.

• Throttling Generation Requests: Implement rate limits on image generation requests to prevent abuse and resource exhaustion.

• Image Compression Security: Verify that image compression methods do not introduce vulnerabilities or quality degradation that could be exploited.

API and Service Security for Image Generation Models

• Image Transformation Security: Secure APIs that perform transformations like resizing, cropping, or color adjustments, ensuring that no arbitrary code execution is possible through them.

• Rate Limiting on Uploads: Implement rate limiting and monitoring on image uploads to prevent DoS attacks through oversized or high-frequency uploads.

• Content Delivery Network (CDN) Security: Use secure CDN configurations for serving generated images, ensuring encryption during transit and secure caching mechanisms.

• Image Processing Sandbox: Run image transformations in a secure sandbox environment to prevent potential exploitation through image-processing libraries.

Adversarial Use and Social Risks Specific to Images

• Synthetic Media Identification: Implement visual indicators or watermarks that clearly identify images as AI-generated, reducing risks of misinformation.

• Misinformation Risk Assessment: Assess the potential for generated images to be used in spreading misinformation or in fraudulent activities.

• Human-in-the-Loop Reviews: For high-risk applications (e.g., media, law enforcement), include human review processes for AI-generated images before they are published.

• Legal Compliance in Image Use: Ensure compliance with laws and regulations around image manipulation and AI-generated media (e.g., Deepfake laws, privacy laws).

Testing for Environmental and Resource Constraints

• GPU/TPU Resource Monitoring: Monitor GPU/TPU usage during image generation to detect unusual spikes that could indicate abuse.

• Memory Management Checks: Ensure the model's memory consumption is controlled to prevent potential overflows or crashes during inference.

• Compute Timeouts: Set timeouts on image generation processes to avoid prolonged generation times leading to resource exhaustion.

Intellectual Property and Licensing

• Training Data Licensing Verification: Ensure that all images used in training adhere to licensing agreements to avoid intellectual property issues.

• Derivative Work Compliance: Verify that generated images respect licensing agreements, especially when generating derivative works based on specific styles or datasets.

• Protecting Artistic Styles: Implement measures to avoid unintended reproduction of specific artists' styles without proper attribution or licensing.

• Third-Party Image Database Security: Verify the security of third-party image databases used in training or as reference material to prevent data leaks.

Advanced Threats Unique to Image Models

• GAN Model Integrity: For models using GANs (Generative Adversarial Networks), ensure that the discriminator and generator models are secure from tampering.

• Feature Space Manipulation: Test if the latent space (feature representations) can be manipulated to produce harmful or inappropriate outputs.

• Model Stealing in Vision Models: Test for potential model extraction attacks where adversaries might use queries to recreate a version of the image generation model.

• Inversion Attacks on Image Models: Evaluate if attackers can reverse-engineer generated images to infer sensitive information from the training set.