Guide-linked quiz · Medium Level
Data Poisoning and Backdoor Attacks Quiz
Compare poisoning and triggered backdoors, with clean-label data, BadNets, mutable web resources, spectral defenses, and trusted integrity hashes.
Question 1 of 8
Which goal distinguishes a triggered backdoor from general availability poisoning?
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Answers and explanations appear as you take the quiz.
- 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
- 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
- What did the BadNets research famously demonstrate?
- A faster training algorithm
- A stop sign misclassified as a speed-limit sign via a sticker trigger
- A new optimizer
- A way to compress models
- How did researchers show web-scale datasets could be poisoned cheaply?
- By hacking cloud servers
- By bribing annotators
- By overloading the network
- By buying expired domains hosting a fraction of dataset image URLs
- 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
- 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
- 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
- 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
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