Questionário sobre envenenamento de dados e ataques backdoor

As respostas e explicações aparecem conforme você responde ao teste.

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. O que a famosa pesquisa da BadNets demonstrou?
   • Um algoritmo de treinamento mais rápido
   • Um sinal de parada classificado incorretamente como sinal de limite de velocidade por meio de um gatilho de adesivo
   • Um novo otimizador
   • Uma maneira de compactar modelos
4. Como os pesquisadores mostraram que conjuntos de dados em escala web poderiam ser envenenados de forma barata?
   • Hackeando servidores em nuvem
   • Subornando anotadores
   • Sobrecarregando a rede
   • Comprando domínios expirados que hospedam uma fração dos URLs de imagens de conjuntos de dados
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