# 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.