Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Sumeynta Xogta iyo Weerarrada Dhabarka ah

Is barbar dhig sunta iyo dib-u-kicinta, xogta summadda nadiifka ah, BadNets, ilaha webka la beddeli karo, difaacyada muuqaalka ah, iyo xashiishyada daacadnimada la aamini karo.

Wadooyinka haga ee la xidhiidhaSumowga Xogta iyo Weerarrada Dhabarka ah
Su'aal 1 ee 8

Which goal distinguishes a triggered backdoor from general availability poisoning?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Which goal distinguishes a triggered backdoor from general availability poisoning?
    • Attacker-chosen behavior associated with a specified trigger
    • A requirement that every clean input must be misclassified
    • A guarantee that all triggered inputs succeed without errors
    • A requirement that only image models can be attacked
  2. What does clean-label poisoning preserve in its modified training examples?
    • A guarantee that every example is harmless
    • Every original input byte with no change
    • A label appropriate to the training item
    • A guarantee that no defense can detect the poison
  3. Maxay cilmi-baarista BadNets si caan ah u muujisay?
    • Algorithm tababarka degdega ah
    • Calaamadda joogsiga ayaa si khaldan loo sifeeyay inay tahay calaamad-xawaaraha iyada oo la adeegsanayo kiciyaha dhejiska ah
    • Wanaajiye cusub
    • Hab lagu cadaadiyo moodooyinka
  4. Sidee bay cilmi-baarayaashu u muujiyeen in xog-ururinta-mareegaha in lagu sumayn karo raqiis?
    • Adigoo jabsaday adeegayaasha daruuraha
    • Adiga oo laaluushaya qorayaasha
    • Adigoo si xad dhaaf ah u saaraya shabakada
    • Adigoo iibsanaya xayndaabyada dhacay ee martigeliyay qayb ka mid ah URL-yada sawirka xogta
  5. What does Spectral Signatures in Backdoor Attacks study as a defense signal?
    • A proof that all larger batches eliminate triggers
    • Only document filenames sorted alphabetically
    • Suspicious statistical structure in learned representations
    • A guarantee that all clean accuracy equals zero
  6. Why can a dataset index pointing to remote content have a split-view poisoning exposure?
    • The indexed resource can change before a later downloader retrieves it
    • Every URL necessarily points to an immutable archived copy
    • All dataset labels are automatically rewritten at inference
    • A domain’s first owner must keep permanent control forever
  7. Which stage is directly manipulated in a data-poisoning attack, as opposed to an inference-only input perturbation?
    • Only the final user-interface font
    • Only a previously fixed output screenshot
    • Only the displayed model name
    • The training data or learning process
  8. What reference makes a content hash useful against the cited split-view poisoning threat?
    • A fresh hash supplied solely by the attacker alongside the changed resource
    • A trusted hash of the raw resource obtained before the malicious change
    • Only an unchanged URL string
    • A requirement that the resource keep the same file extension