Adversarial Mienzaniso uye Robustness Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Under a specified threat model, what is an adversarial example?
   • An input crafted to cause a model error using changes allowed by that threat model
   • Any ordinary prediction error on unmodified input
   • An input that must contain tiny, imperceptible pixel changes
   • A model update that improves accuracy on every distribution
2. Chii chinosiyanisa kurwiswa kwe 'white-box' kubva pakurwisa 'kwebhokisi dema'?
   • White-box kurwisa kunoshanda chete pamifananidzo michena
   • Mubhokisi-chena, anorwisa anokwanisa kuwana mukati meiyo modhi / gradients; mubhokisi-dema, zvinobuda chete
   • Kurwiswa kweBlack-box kunogara kuchikurumidza
   • White-box kurwisa hakudi computation
3. FGSM (Fast Gradient Sign Method) inogadzira muenzaniso weanopikisa ne...
   • Randomly shuffling pixels
   • Kudzidzirazve modhi kubva pakutanga
   • Kutora nhanho munzira yechiratidzo chekurasika gradient maererano nekuisa
   • Kubvisa mufananidzo
4. What is adversarial training commonly used for?
   • Guaranteeing robustness to every possible attack
   • Proving a classifier is fair
   • Eliminating all clean-data errors
   • Improving robustness against the adversarial threat model represented during training
5. Sei 'kuchinjisa' kwemienzaniso yeanopikisa kune chekuita?
   • Kurwisa kwakagadzirwa pane imwe modhi kunowanzo pusa mamwe mamodheru, zvichiita kuti dema-bhokisi kurwisa
   • Inoita kuti mamodheru aite zvishoma nezvishoma
   • Inowedzera mari yekuchengetedza
   • Inongobata maodhiyo modhi
6. How are prompt-injection attacks related to image adversarial examples?
   • They are identical bounded pixel perturbations
   • Both exploit model vulnerabilities, but prompt injection has a distinct instruction and system threat model
   • Prompt injection requires changing model weights
   • Image adversarial examples only affect text prompts
7. Why must an adversarial-robustness report specify the perturbation norm and budget?
   • They determine the model’s number of parameters
   • They guarantee transfer to every attack
   • They define which input changes the reported robustness claim covers
   • They replace the need to state an attacker’s access
8. Which pair of measures helps distinguish clean performance from performance under a specified attack?
   • Training-set size and GPU memory
   • Tokenization rate and context length
   • Model file size and parameter dtype
   • Clean accuracy and robust accuracy under the stated attack setting