SECURITY BREACH /// FGSM EVASION /// DATA POISONING /// MODEL EXTRACTION /// DEFENSE PROTOCOLS /// SECURITY BREACH ///

Adversarial
Attacks

Discover how fragile neural networks truly are. Master evasion attacks, understand data poisoning, and implement critical defenses to secure your models.

attack_vector.py
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Director:Machine Learning models are powerful, but incredibly fragile. They see the world through numbers, making them vulnerable to invisible attacks.


Threat Matrix

UNLOCK MODULES TO DEFEND THE SYSTEM.

Phase 1: Evasion

Evasion attacks occur during inference. The attacker manipulates input data to trick a deployed, trained model.

System Audit

Which of the following is an evasion attack?

Securing the Black Box:
Adversarial Attacks

Machine learning models are mathematical optimizers. They do not "see" a stop sign; they compute pixel matrices. Adversarial attacks exploit the massive dimensionality of these matrices, tricking models into devastating misclassifications with changes completely invisible to humans.

Evasion Attacks (The FGSM Algorithm)

Evasion attacks happen during deployment (inference). The attacker feeds manipulated data into an already trained model to force a wrong prediction. The most famous technique is the Fast Gradient Sign Method (FGSM).

FGSM utilizes the gradients of the neural network to create adversarial examples. For an input image $x$ and a target label $y$, the adversarial image $x'$ is computed as:

$$x' = x + \epsilon \cdot \text&123;sign&125;(\nabla_x J(\theta, x, y))$$

Here, $\epsilon$ (epsilon) is the attack strength, $J$ is the loss function, and $\theta$ are the model weights. The noise is pushed in the exact direction that maximizes the error.

Data Poisoning (Training Phase)

If an attacker can alter the training dataset before the model is even built, they execute a poisoning attack. By injecting maliciously labeled data or inserting "backdoor triggers" (like a small yellow square on images), the model learns a corrupted logic.

Later, in the real world, the model operates normally—until it sees the backdoor trigger, which causes it to execute the attacker's embedded payload.

📡 Intelligence Briefing (FAQ)

What is the difference between Evasion and Poisoning attacks?

Evasion occurs during inference (after training). The attacker modifies the input data (e.g., placing stickers on a stop sign). Poisoning occurs during training. The attacker alters the dataset itself to embed backdoors or ruin model accuracy before deployment.

How does Adversarial Training defend against attacks?

Adversarial Training is a brute-force defense strategy. The defender generates adversarial examples (like adding FGSM noise to images) and includes them in the training dataset alongside their correct labels. This forces the model to learn robust features rather than relying on brittle pixel patterns.

What does Epsilon ($\epsilon$) do in FGSM?

Epsilon is the perturbation multiplier. It dictates how much noise is added to the image. A very high epsilon will break the model easily, but the attack will be obvious to a human. A low epsilon creates an attack that humans cannot perceive, but is harder to trick the model with.

Threat Glossary

Adversarial Attack
Techniques used to fool machine learning models by providing deceptive input data.
FGSM
Fast Gradient Sign Method. A white-box evasion attack that uses the model's gradients to maximize error.
White-Box Attack
An attack scenario where the attacker has full access to the model's architecture, weights, and gradients.
Black-Box Attack
An attack scenario where the attacker only has access to the model's inputs and outputs.
Data Poisoning
Manipulating the training dataset to compromise the learning process and create backdoors.
Adversarial Training
A defense mechanism involving training the model on adversarial examples to increase robustness.