In the era of generative AI, the widespread adoption of Neural Text Generators (NTGs) presents new cybersecurity challenges, particularly within the realms of Digital Forensics and Incident Response (DFIR). These challenges primarily involve the detection and attribution of sources behind advanced attacks like spearphishing and disinformation campaigns. As NTGs evolve, the task of distinguishing between human and NTG-authored texts becomes critically complex. This paper rigorously evaluates the DFIR pipeline tailored for text-based security systems, specifically focusing on the challenges of detecting and attributing authorship of NTG-authored texts. By introducing a novel human-NTG co-authorship text attack, termed CS-ACT, our study uncovers significant vulnerabilities in traditional DFIR methodologies, highlighting discrepancies between ideal scenarios and real-world conditions. Utilizing 14 diverse datasets and 43 unique NTGs, up to the latest GPT-4, our research identifies substantial vulnerabilities in the forensic profiling phase, particularly in attributing authorship to NTGs. Our comprehensive evaluation points to factors such as model sophistication and the lack of distinctive style within NTGs as significant contributors for these vulnerabilities. Our findings underscore the necessity for more sophisticated and adaptable strategies, such as incorporating adversarial learning, stylizing NTGs, and implementing hierarchical attribution through the mapping of NTG lineages to enhance source attribution. This sets the stage for future research and the development of more resilient text-based security systems.

Location trajectories provide valuable insights for applications from urban planning to pandemic control. However, mobility data can also reveal sensitive information about individuals, such as political opinions, religious beliefs, or sexual orientations. Existing privacy-preserving approaches for publishing this data face a significant utility-privacy trade-off. Releasing synthetic trajectory data generated through deep learning offers a promising solution. Due to the trajectories' sequential nature, most existing models are based on recurrent neural networks (RNNs). However, research in generative adversarial networks (GANs) largely employs convolutional neural networks (CNNs) for image generation. This discrepancy raises the question of whether advances in computer vision can be applied to trajectory generation. In this work, we introduce a Reversible Trajectory-to-CNN Transformation (RTCT) that adapts trajectories into a format suitable for CNN-based models. We integrated this transformation with the well-known DCGAN in a proof-of-concept (PoC) and evaluated its performance against an RNN-based trajectory GAN using four metrics across two datasets. The PoC was superior in capturing spatial distributions compared to the RNN model but had difficulty replicating sequential and temporal properties. Although the PoC's utility is not sufficient for practical applications, the results demonstrate the transformation's potential to facilitate the use of CNNs for trajectory generation, opening up avenues for future research. To support continued research, all source code has been made available under an open-source license.

Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact, especially a text t in question, an AA solution aims to accurately attribute t to its true author out of many candidate authors while an AO solution aims to modify t to hide its true authorship. Traditionally, the notion of authorship and its accompanying privacy concern is only toward human authors. However, in recent years, due to the explosive advancements in Neural Text Generation (NTG) techniques in NLP, capable of synthesizing human-quality openended texts (so-called "neural texts"), one has to now consider Figure 1: The figure illustrates the quadrant of research problems authorships by humans, machines, or their combination. Due where (1) the GRAY quadrants are the focus of this survey, to the implications and potential threats of neural texts when and (2) The BLACK box indicates the specialized binary AA problem used maliciously, it has become critical to understand the limitations to distinguish neural texts from human texts. of traditional AA/AO solutions and develop novel AA/AO solutions in dealing with neural texts. In this survey, therefore, we make a comprehensive review of recent literature on the attribution released (e.g., FAIR [16, 82], CTRL [59], PPLM [25], T5 [94], Wu-and obfuscation of neural text authorship from a Data Dao

Our goal is for a robot to execute a previously unseen task based on a single video demonstration of the task. The success of our approach relies on the principle of transferring knowledge from seen tasks to unseen ones with similar semantics. More importantly, we hypothesize that to successfully execute a complex task from a single video demonstration, it is necessary to explicitly incorporate compositionality to the model. To test our hypothesis, we propose Neural Task Graph (NTG) Networks, which use task graph as the intermediate representation to modularize the representations of both the video demonstration and the derived policy. We show this formulation achieves strong inter-task generalization on two complex tasks: Block Stacking in BulletPhysics and Object Collection in AI2-THOR. We further show that the same principle is applicable to real-world videos. We show that NTG can improve data efficiency of few-shot activity understanding in the Breakfast Dataset.