An Activation Function is a mathematical function applied to neural network nodes to introduce non-linearity into the model. It enables the network to learn complex patterns by transforming input signals into output values. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, and Tanh, each suited for specific tasks like classification or regression. Without activation functions, neural networks would be limited to linear mappings, reducing their effectiveness in tasks like image recognition and natural language processing.

Adversarial Examples are specifically designed inputs intended to deceive deepfake detection models by exploiting their vulnerabilities. These examples subtly alter input data in ways that cause AI models to make incorrect classifications while appearing unchanged to humans. Adversarial attacks highlight the need for robust deepfake detection systems capable of recognizing such manipulations and enhancing system security through continuous model improvement and adversarial training.

Adversarial Training is a machine learning technique that improves model robustness by exposing it to adversarial examples—intentionally modified inputs designed to mislead the model. This method strengthens the model’s ability to handle unexpected or malicious inputs by training it to recognize and resist such perturbations. Adversarial training is widely used in security-critical applications such as fraud detection, autonomous driving, and deepfake detection.

An Algorithm is a defined set of instructions or rules designed to solve specific problems or perform tasks. In machine learning, algorithms process input data, identify patterns, and generate predictions or decisions. Different algorithms are suited for various tasks, such as classification, regression, clustering, and optimization. Choosing the right algorithm involves balancing performance, interpretability, and computational efficiency based on the problem at hand.

Artificial Intelligence refers to the development of computer systems capable of performing tasks that typically require human intelligence. These tasks include learning from experience, reasoning through complex situations, understanding language, and making decisions. AI systems rely on algorithms and data to simulate cognitive functions, driving advancements in various fields such as healthcare, finance, and autonomous systems.

The Attention Mechanism is a neural network technique that allows models to focus on the most relevant parts of an input sequence when generating output. It assigns varying importance to different input segments, enabling context-aware processing. This mechanism is foundational in tasks like machine translation, text summarization, and speech recognition, powering models such as Transformers and attention-based RNNs.