Perturbations and image evasion attack techniques
Perturbations are essential to deceiving ML models in evasion attacks. Perturbations are crafted modifications that cause a model to make incorrect predictions when applied to input data. Perturbations are crafted using advanced calculations to make them as imperceptible as possible, and this can make them highly effective in escaping the attention of humans or even AI systems.
This subtle manipulation of data is central to evasion tactics, aiming to either confound the model entirely (untargeted attacks) or misguide it to a specific, erroneous outcome (targeted attacks). The sophistication of these techniques lies in their ability to alter the data imperceptibly to human observers while leading the AI astray—a trait that underscores their potential danger and the necessity for robust defenses.
Generating perturbations relies on the precise calculation of adversarial AI using optimization techniques that involve gradient...
