Controllable Fake Document Infilling for Cyber Deception
Yibo Hu, Lin Yu, Erick Skorupa Parolin, Latifur Khan, Kevin W. Hamlen · 2022
Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information.However, existing approaches are context-agnostic, resulting in sub-optimal and unvaried outputs.We propose a novel context-aware model, Fake Document Infilling (FDI), by converting the problem to a controllable mask-then-infill procedure.FDI masks important concepts of varied lengths in the document, then infills a realistic but fake alternative considering both the previous and future contexts.We conduct comprehensive evaluations on technical documents and news stories.Results show that FDI outperforms the baselines in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries. A. Original Article Tomographic Image Reconstruction using Training imagesWe describe and examine an algorithm for tomographic image reconstruction where prior knowledge about the solution is available in the form of training images.We first construct a nonnegative dictionary based on prototype elements from the training images; this problem is formulated as a regularized non-negative matrix.Incorporating the dictionary as a prior in a convex reconstruction problem, we then find an approximate solution with a sparse representation in the dictionary... B. GPT-2: Generation given a prompt ...We describe and examine an algorithm for tomographic image reconstruction where prior knowledge about the solution is available in the form of training images.Instances were reconstructed from their images using image and pixel centroids.The concept of image reconstruction provides several advantages over previous techniques, such as indexing the solution to a representation with integral or submaximal number of cepstrates, ... C. WEF-Replacing nouns ...We first construct a nonnegative dictionary based on prototype elements from the training images; this problem is formulated as a regularized non-negative matrix factorization.Incorporating the dictionary as a prior in a convex reconstruction problem, we then find an approximate solution with a sparse representation in the dictionary... D. WEF-Generation ...We first construct a nonnegative sparsity based on prototype elements from the encoderdecoder images; this problem is formulated as a regularized non-negative matrix orthonormal.Incorporating the sparsity as a prior in a convex reconstruction problem, we then find an approximate strategy with a sparse representation in the sparsity... E. FDI-Replacing n-grams ...We first construct a nonnegative dictionary based on prototype elements from the training images; this problem is formulated as a regularized non-negative matrix factorization.Incorporating the dictionary as a prior in a convex reconstruction problem, we then find an approximate solution with a sparse representation in the dictionary... F. FDI-Generation ...We first construct a collection of missing patches based on images from the training images; this problem is formulated as a regularized non-negative matrix factorization.Incorporating the dictionary as a prior in the whole dictionary, we then find a similar estimate for missing patches in the dictionary...