A Girl Has A Name: Detecting Authorship Obfuscation
- URL: http://arxiv.org/abs/2005.00702v1
- Date: Sat, 2 May 2020 04:52:55 GMT
- Title: A Girl Has A Name: Detecting Authorship Obfuscation
- Authors: Asad Mahmood, Zubair Shafiq and Padmini Srinivasan
- Abstract summary: Authorship attribution aims to identify the author of a text based on the stylometric analysis.
Authorship obfuscation aims to protect against authorship attribution by modifying a text's style.
We evaluate the stealthiness of state-of-the-art authorship obfuscation methods under an adversarial threat model.
- Score: 12.461503242570643
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Authorship attribution aims to identify the author of a text based on the
stylometric analysis. Authorship obfuscation, on the other hand, aims to
protect against authorship attribution by modifying a text's style. In this
paper, we evaluate the stealthiness of state-of-the-art authorship obfuscation
methods under an adversarial threat model. An obfuscator is stealthy to the
extent an adversary finds it challenging to detect whether or not a text
modified by the obfuscator is obfuscated - a decision that is key to the
adversary interested in authorship attribution. We show that the existing
authorship obfuscation methods are not stealthy as their obfuscated texts can
be identified with an average F1 score of 0.87. The reason for the lack of
stealthiness is that these obfuscators degrade text smoothness, as ascertained
by neural language models, in a detectable manner. Our results highlight the
need to develop stealthy authorship obfuscation methods that can better protect
the identity of an author seeking anonymity.
Related papers
- TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization Methods [5.239989658197324]
Authorship obfuscation aims to disguise the identity of an author within a text.
This alteration needs to balance privacy and utility.
We propose TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization.
arXiv Detail & Related papers (2024-07-31T14:24:01Z) - Provably Secure Disambiguating Neural Linguistic Steganography [66.30965740387047]
The segmentation ambiguity problem, which arises when using language models based on subwords, leads to occasional decoding failures.
We propose a novel secure disambiguation method named SyncPool, which effectively addresses the segmentation ambiguity problem.
SyncPool does not change the size of the candidate pool or the distribution of tokens and thus is applicable to provably secure language steganography methods.
arXiv Detail & Related papers (2024-03-26T09:25:57Z) - Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery
Detection [62.595450266262645]
This paper introduces a novel and previously unrecognized threat in face forgery detection scenarios caused by backdoor attack.
By embedding backdoors into models, attackers can deceive detectors into producing erroneous predictions for forged faces.
We propose emphPoisoned Forgery Face framework, which enables clean-label backdoor attacks on face forgery detectors.
arXiv Detail & Related papers (2024-02-18T06:31:05Z) - JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding
over Small Language Models [53.83273575102087]
We propose an unsupervised inference-time approach to authorship obfuscation.
We introduce JAMDEC, a user-controlled, inference-time algorithm for authorship obfuscation.
Our approach builds on small language models such as GPT2-XL in order to help avoid disclosing the original content to proprietary LLM's APIs.
arXiv Detail & Related papers (2024-02-13T19:54:29Z) - UID as a Guiding Metric for Automated Authorship Obfuscation [0.0]
Automated authorship attributors are capable of attributing the author of a text amongst a pool of authors with great accuracy.
In order to counter the rise of these automated attributors, there has also been a rise of automated obfuscators.
We devised three novel authorship obfuscation methods that utilize a Psycho-linguistic theory known as Uniform Information Density (UID) theory.
arXiv Detail & Related papers (2023-11-05T22:16:37Z) - Can AI-Generated Text be Reliably Detected? [54.670136179857344]
Unregulated use of LLMs can potentially lead to malicious consequences such as plagiarism, generating fake news, spamming, etc.
Recent works attempt to tackle this problem either using certain model signatures present in the generated text outputs or by applying watermarking techniques.
In this paper, we show that these detectors are not reliable in practical scenarios.
arXiv Detail & Related papers (2023-03-17T17:53:19Z) - Zero-Query Transfer Attacks on Context-Aware Object Detectors [95.18656036716972]
Adversarial attacks perturb images such that a deep neural network produces incorrect classification results.
A promising approach to defend against adversarial attacks on natural multi-object scenes is to impose a context-consistency check.
We present the first approach for generating context-consistent adversarial attacks that can evade the context-consistency check.
arXiv Detail & Related papers (2022-03-29T04:33:06Z) - A Girl Has A Name, And It's ... Adversarial Authorship Attribution for
Deobfuscation [9.558392439655014]
We show that adversarially trained authorship attributors are able to degrade the effectiveness of existing obfuscators.
Our results underline the need for stronger obfuscation approaches that are resistant to deobfuscation.
arXiv Detail & Related papers (2022-03-22T16:26:09Z) - Protecting Anonymous Speech: A Generative Adversarial Network
Methodology for Removing Stylistic Indicators in Text [2.9005223064604078]
We develop a new approach to authorship anonymization by constructing a generative adversarial network.
Our fully automatic method achieves comparable results to other methods in terms of content preservation and fluency.
Our approach is able to generalize well to an open-set context and anonymize sentences from authors it has not encountered before.
arXiv Detail & Related papers (2021-10-18T17:45:56Z) - Avengers Ensemble! Improving Transferability of Authorship Obfuscation [7.962140902232626]
Stylometric approaches have been shown to be quite effective for real-world authorship attribution.
We propose an ensemble-based approach for transferable authorship obfuscation.
arXiv Detail & Related papers (2021-09-15T00:11:40Z) - InfoScrub: Towards Attribute Privacy by Targeted Obfuscation [77.49428268918703]
We study techniques that allow individuals to limit the private information leaked in visual data.
We tackle this problem in a novel image obfuscation framework.
We find our approach generates obfuscated images faithful to the original input images, and additionally increase uncertainty by 6.2$times$ (or up to 0.85 bits) over the non-obfuscated counterparts.
arXiv Detail & Related papers (2020-05-20T19:48:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.