8.3.3 Bypassing Surveillance Systems

Modern AI-powered surveillance has moved far beyond simple recording. These systems now actively perceive, interpret, and make decisions based on video feeds. This interpretative layer is the new battleground. Your objective as a red teamer is not merely to evade detection but to actively manipulate the system’s understanding of reality. This chapter details the primary attack vectors for achieving this, turning a system’s intelligence into a liability.

A Taxonomy of Evasion Techniques

Bypassing a surveillance system can be accomplished through several distinct methods, each exploiting a different part of the AI pipeline. We can categorize these into three primary families: physical-world perturbations, data-level manipulation, and temporal logic attacks.

1. Adversarial Cloaking: Physical Perturbations

This is the most direct method of attack: introducing a specially crafted object or pattern into the physical world to cause misclassification at inference time. The most common example is the “adversarial patch,” a printed image designed to be worn or carried.

The core principle involves creating a pattern that maximizes the classification loss for a target class (e.g., “person”) while simultaneously maximizing the confidence for an incorrect class (e.g., “backpack,” “umbrella,” or simply nothing). The key challenge is creating a patch that is robust to real-world transformations: changes in angle, lighting, distance, and motion blur.

To generate such a patch, you would typically use an optimization algorithm that iteratively adjusts the patch’s pixel values. This process, often called “Expectation Over Transformation” (EOT), involves rendering the patch onto a 3D model or image of a person and then applying a series of random transformations (scaling, rotation, lighting changes) before feeding it to the target model.

# Pseudocode for generating a physical adversarial patch
import torch
from torchvision import transforms

# target_model: The surveillance model to attack (e.g., YOLO)
# patch_size: The resolution of the patch to generate
patch = torch.rand(3, patch_size, patch_size, requires_grad=True)
optimizer = torch.optim.Adam([patch], lr=0.01)

# EOT: Apply random transformations to simulate real-world conditions
transform = transforms.Compose([
    transforms.RandomAffine(degrees=20, translate=(0.1, 0.1)),
    transforms.ColorJitter(brightness=0.3, contrast=0.3),
])

for i in range(1000): # Optimization loop
    # Apply patch to a clean image of a person
    # Apply random transformations via `transform`
    # Feed the transformed image to the `target_model`
    # Calculate loss (e.g., maximize "toaster" class, minimize "person" class)
    loss.backward()
    optimizer.step()
    # Ensure patch pixel values remain valid (0-1 range)
    patch.data.clamp_(0, 1)

2. Data-Level Evasion: Poisoning and Backdoors

A more covert and persistent bypass involves corrupting the model before it’s even deployed. By poisoning the training dataset, you can create a “blind spot” or a backdoor that the model learns implicitly. This is a supply chain attack on the AI model itself.

For example, you could inject hundreds of images into a training set where individuals wearing a specific, non-threatening item (like a particular brand of hat) are mislabeled or not labeled at all. The resulting model will learn an association: “if this hat is present, the object is not a person.” This bypass is highly effective because it’s not an artifact of noisy input; it’s a fundamental flaw in the model’s learned logic.

3. Temporal Logic Manipulation

Surveillance systems are increasingly analyzing video as a sequence, not just a series of independent frames. They use tracking algorithms (like DeepSORT or ByteTrack) to maintain object identity over time and action recognition models to classify behavior. This temporal dimension opens up new attack surfaces.

A prime example is a “Stop-and-Go” attack. An actor moves, then freezes unnaturally for a few seconds, then moves again. A simple tracker might interpret the static phase as the object disappearing (or becoming part of the background) and the subsequent movement as a new object appearing. This breaks the continuity of the track, assigning a new ID to the same person and disrupting any long-term analysis.

Red Teaming Methodology and Tools

Executing these attacks requires a structured approach. Simply printing a random pattern from a research paper is unlikely to succeed against a production system.

The Surveillance Bypass Kill Chain

1. Reconnaissance: Identify the target system. Is it a commercial product or custom-built? What models are likely in use? Open-source intelligence (e.g., public tenders, documentation) can reveal if a city is using systems based on common architectures like YOLO for detection and SORT for tracking.
2. Model Replication: You rarely have direct access to the target model. The goal is to build a local surrogate model that is functionally similar. Train a public model (like YOLOv8) on a similar public dataset (like COCO or MOTChallenge) to approximate the target’s behavior.
3. Attack Crafting: Use your surrogate model to generate the bypass artifact. For physical patches, this involves the EOT optimization process. For temporal attacks, this involves scripting scenarios in a simulator.
4. Digital & Simulated Testing: Before physical deployment, test extensively. Use simulators like NVIDIA Isaac Sim or CARLA to place your 3D-rendered patch in a virtual environment under various conditions. This iterative refinement is critical for real-world success.
5. Physical Deployment & Analysis: The final step is to test the bypass in the physical world. This requires careful observation and analysis of the system’s response, which may be subtle (e.g., a lower confidence score, a brief track interruption).

Tooling

• Attack Generation Frameworks: The Adversarial Robustness Toolbox (ART) from IBM is a standard for crafting digital adversarial examples that can be adapted for the initial stages of physical attack design.
• Simulators: CARLA (for autonomous driving scenarios) and NVIDIA Isaac Sim (for robotics and vision AI) are powerful tools for testing physical attacks without needing physical access.
• Hardware: A high-quality color printer is essential for physical patches. The material used (e.g., matte vs. glossy paper) can significantly impact the patch’s effectiveness by affecting reflections.