Aggregation-Aware Synthetic Text Generation Against Authorship Re-Identification

Qian Ma; Anna Squicciarini; Sarah Rajtmajer

cs.AIAug 23, 2026

Aggregation-Aware Synthetic Text Generation Against Authorship Re-Identification

Authors: Qian Ma, Anna Squicciarini, Sarah Rajtmajer

Organizations: Information Sciences and Technology The Pennsylvania State University

Abstract

Online users often release multiple texts under the same identity, giving attackers an author profile that can reveal more than any single text. Existing authorship obfuscation methods optimize privacy independently for each document, leaving them blind to cross-document correlations that make aggregation dangerous. We propose Aggregation-Aware Synthetic Text Generation (AAST), a framework that addresses this gap by jointly selecting synthetic texts at the bundle level rather than optimizing each text in isolation. AAST targets attribution and verification attacks, including cross-genre settings where attacker references come from a genre not observed during generation or selection. Experiments across same-genre, cross-genre, neural, and independent non-neural stylometric attacks show that AAST lowers account-level linkability as bundle size grows, while preserving semantic quality, linguistic acceptability, and sentiment alignment.

Figures & tables

Figure 1: Overview of the AAST pipeline for aggregation-aware synthetic text generation.

Panel of Figure 2 — Figure 2: Attribution risk, verification risk, NLI, and CoLA across bundle sizes on Reddit. In (c), each synthetic output is evaluated against its corresponding private text, so the private text serves as the reference and is not plotted.

Figure 3: Verification results under a stylometric transfer attack on Reddit and Blog.

Genre Pair	Attack	Method	Acc. ( $\downarrow$ )	R@8 ( $\downarrow$ )	Avg.Rank ( $\uparrow$ )
Tweet–Article	SELMA + TaskOnly	AAST M	1.2	7.5	173
		AAST Φ	1.9	7.6	175
		KiP	2.7	9.8	157
		JAMDEC	2.2	11.3	121
	SELMA + LIP	AAST M	0.9	5.0	192
		AAST Φ	1.6	6.6	185

Table 1: Cross-genre authorship attribution risk results.

Panel of Figure 4 — Figure 4: Attribution risk, verification risk, NLI, and CoLA across bundle sizes on Blog. In (c), each synthetic output is evaluated against its corresponding private text, so the private text serves as the reference and is not plotted.

Appendix figures & tables22 assets

Supplementary material from the paper’s appendix.

Appendix

Model or Parameter	Value	Purpose
Abstraction model (§ 3.4 )	facebook/bart-large-cnn DBLP:conf/acl/LewisLGGMLSZ20	Generates abstraction candidates from each private text.
Sentiment classifier model (§ 3.4 )	sentiment-roberta-large-en hartmann2023	Checks whether abstraction candidates preserve the private text sentiment.
Semantic scoring model (§ 3.4.2 )	sentence-t5-base ni2021sentencet5scalablesentenceencoders	Computes semantic similarity between the private text and abstraction candidates.
Generator (§ 3.5 )	Mistral-Small mistralai2025mistralsmall32 and Phi-4 DBLP:journals/corr/abs-2412-08905	Generates synthetic candidate rewrites from selected abstractions.
Style embedding model (§ 3.6 )	intfloat/e5-base-v2 wang2022text	Filters candidates that remain too close to the private input in stylistic space.
Episode encoder model (§ 3.7.1 )	Luar-mud rivera2021learning	Computes bundle embeddings for partner and crowd similarity scoring.

Appendix

Table 2: Models and fixed implementation settings for AAST. These values are held constant across all experiments.

$K$	Method	Reddit		Blog
		MRR ( $\downarrow$ )	R@8 ( $\downarrow$ )	MRR ( $\downarrow$ )	R@8 ( $\downarrow$ )
1	Private	32.2	45.6	38.5	52.4
	AAST Φ	2.4	3.6	1.7	2.0
	AAST M	2.2	2.9	2.6	2.4
	JAMDEC	4.5	8.4	7.4	12.8
	KiP	9.4	17.6	8.2	12.8

Appendix

Table 3: Attribution risk results on Reddit and Blog across bundle sizes.

$K$	Method	Reddit		Blog
		AUC ( $\approx$ 50)	c@1 ( $\downarrow$ )	AUC ( $\approx$ 50)	c@1 ( $\downarrow$ )
1	Private	51.9	52.9	64.6	60.6
	AAST Φ	50.6	51.7	48.3	48.6
	AAST M	48.9	44.6	47.6	49.3
	JAMDEC	51.2	52.0	60.8	53.4
	KiP	51.3	52.6	63.2	57.7

Appendix

Table 4: Verification risk results on Reddit and Blog across bundle sizes.

$K$	Method	Reddit		Blog
		MRR ( $\downarrow$ )	R@8 ( $\downarrow$ )	MRR ( $\downarrow$ )	R@8 ( $\downarrow$ )
1	Private	17.5 [14.1, 20.9]	28.0 [23.6, 32.8]	14.2 [11.2, 17.4]	25.6 [21.2, 30.4]
	AAST Φ	3.9 [2.6, 5.4]	6.4 [4.6, 8.6]	3.4 [2.2, 4.8]	3.6 [2.4, 5.2]
	AAST M	5.3 [3.8, 6.8]	8.9 [6.6, 11.6]	2.4 [1.6, 3.4]	3.6 [2.4, 5.0]
	JAMDEC	4.2 [2.8, 6.0]	5.6 [3.8, 7.8]	7.9 [5.8, 10.4]	16.0 [12.6, 19.8]
	KiP	7.8 [5.6, 10.2]	11.6 [8.6, 14.8]	5.1 [3.6, 6.8]	8.8 [6.4, 11.6]

Appendix

Table 5: Same-genre attribution results on Reddit and Blog under an independent classical n-gram stylometric attacker. The attributor uses character 2–5-grams, distance-character 1–3-grams, and word 1–3-grams. Values are percentages with 95% bootstrap confidence intervals computed over users.

$K$	Method	Reddit		Blog
		AUC $\approx$ 50	c@1 $\downarrow$	AUC $\approx$ 50	c@1 $\downarrow$
1	Private	54.2 [47.2, 61.0]	50.8 [44.4, 56.8]	60.0 [52.6, 66.7]	51.6 [45.6, 57.6]
	AAST M	52.5 [45.5, 59.9]	49.2 [43.0, 55.4]	50.2 [43.2, 57.3]	50.8 [44.8, 56.9]
	AAST Φ	49.8 [42.6, 57.5]	49.4 [43.0, 55.4]	50.3 [43.1, 57.9]	49.2 [42.8, 55.2]
	JAMDEC	50.5 [43.3, 57.8]	48.4 [42.4, 54.8]	64.0 [57.2, 70.4]	59.2 [52.8, 65.6]
	KiP	53.7 [46.2, 61.2]	48.8 [42.0, 54.8]	50.3 [42.8, 57.7]	50.0 [43.6, 56.0]

Appendix

Table 6: Same-genre verification results under a classical stylometric transfer attack on Reddit and Blog. The verifier uses character 3–5-gram term frequency–inverse document frequency features with logistic regression. Values are percentages with 95% bootstrap confidence intervals.

$K$	Method	Reddit		Blog
		NLI ( $\uparrow$ )	CoLA ( $\uparrow$ )	NLI ( $\uparrow$ )	CoLA ( $\uparrow$ )
1	AAST Φ	55.1	94.7	55.0	93.6
	AAST M	53.4	93.1	51.6	93.4
	JAMDEC	20.2	91.4	20.0	90.0
	KiP	65.4	63.2	58.1	55.8
2	AAST Φ	57.2	94.9	57.4	93.9

Appendix

Table 7: NLI and CoLA results on Reddit and Blog across bundle sizes.

$K$	Method	Sentiment Alignment( $\%$ ) ( $\uparrow$ )
		Reddit	Blog
1	AAST Φ	83.0	83.6
	AAST M	83.8	87.8
2	AAST Φ	83.7	85.7
	AAST M	84.7	87.6
4	AAST Φ	85.3	85.0

Appendix

Table 8: Sentiment alignment results on Reddit and Blog across bundle sizes. Higher sentiment alignment indicates better preservation of private text sentiment.

$K$	Method	Reddit						Blog
		Attribution		Verification		Utility		Attribution		Verification		Utility
		R@8 ( $\downarrow$ )	MRR ( $\downarrow$ )	AUC ( $\approx$ 50)	c@1 ( $\downarrow$ )	NLI ( $\uparrow$ )	CoLA ( $\uparrow$ )	R@8 ( $\downarrow$ )	MRR ( $\downarrow$ )	AUC ( $\approx$ 50)	c@1 ( $\downarrow$ )	NLI ( $\uparrow$ )	CoLA ( $\uparrow$ )
1	Private	45.6	32.2	51.9	52.9	–	–	52.4	38.5	64.6	60.6	–	–
	AAST Φ	3.6	2.4	50.6	51.7	55.1	94.7	2.0	1.7	48.3	48.6	55.0	93.6
	AAST M	2.9	2.2	48.9	44.6	53.4	93.1	2.4	2.6	47.6	49.3	51.6	93.4
	RUPTA Φ	8.4	14.5	51.0	47.9	62.1	85.8	26.4	16.8	59.6	57.6	60.5	84.5

Appendix

Table 9: Comparison with the LLM-based RUPTA baseline on Reddit and Blog across

K

=1,8,16. Utility is not reported for Private.

Genre Pair	Attack	Method	Acc. ( $\downarrow$ )	F1 ( $\downarrow$ )
Tweet–Article	SELMA + TaskOnly	AAST M	54.8	54.3
		AAST Φ	55.6	53.6
		KiP	58.2	56.8
		JAMDEC	55.0	62.9
	SELMA + PromptAV	AAST M	55.2	53.9
		AAST Φ	55.9	56.2

Appendix

Table 10: Cross-genre authorship verification risk results.

Genre Pair	Method	NLI ( $\uparrow$ )	CoLA ( $\uparrow$ )
Tweet – Article	AAST M	27.6	89.6
	AAST Φ	30.9	83.2
	KiP	53.8	53.9
	JAMDEC	18.3	71.4
Article – Tweet	AAST M	14.9	86
	AAST Φ	18.0	80.3

Appendix

Table 11: Cross-genre NLI and CoLA results.

ID	Masked Tweet Example
1	@[USER1] @[USER2] @[USER3] @[USER4] @[USER5] @[USER6] Congratulations to all fellow winners
2	#NewProfilePic [URL]
3	@[USER7] No
4	@[USER8] haha

Appendix

Table 12: Masked examples of tweet inputs from one author. Handles and URLs are replaced with placeholders.

$K$	$\sigma$
	$\epsilon$ = 4	$\epsilon$ = 2	$\epsilon$ = 1
1	1.02	2.03	4.07
8	1.15	2.31	4.61
16	1.19	2.39	4.78

Appendix

Table 13: Gaussian noise standard deviations

\sigma

used for noisy abstraction selection.

Bundle Size $K$	Total Tokens $(\times 10^{6})$
1	0.50
2	1.01
4	2.00
6	3.00
8	3.99
10	4.99

Appendix

Table 14: Token usage of AAST M on Blog across bundle sizes. Counts include both input and output tokens for Mistral generation.

Ablation configuration	$K$	Attribution		Verification		Utility
		R@8 ( $\downarrow$ )	MRR ( $\downarrow$ )	AUC ( $\approx$ 50)	c@1 ( $\downarrow$ )	NLI ( $\uparrow$ )	CoLA ( $\uparrow$ )
Identifier Masking(§ 3.3 ) enabled	1	0	1.2	55.6	52.6	18.8	92.5
	8	3.3	2.0	55.8	46.4	38.4	92.8
	16	13.5	8.2	51.3	46.5	50.6	92.7
Identifier Masking disabled	1	2.9	2.2	48.9	44.6	53.4	93.1
	8	28.4	18.4	64.2	48.2	65.7	94.1

Appendix

Table 15: Ablation study of AAST components on Reddit across

K=1,8,16

. The first two row groups compare Identifier Masking configurations. The last three row groups disable one component relative to the Identifier Masking enabled configuration. In the reported main experiments, same-genre Reddit and Blog use Identifier Masking disabled, while cross-genre Article–Tweet and Tweet–Article use Identifier Masking enabled. Candidate Preselection, Refinement, and Bundle-Level Selection are enabled in all reported main AAST settings. Bundle-Level Selection consists of Bundle-level Candidate Scoring followed by Candidate Refinement.

Figure 7: Effect of removing bundle-level selection on Reddit and Blog. NoBundleSel Φ keeps the same settings as AAST Φ , but removes Bundle-level Candidate Scoring and Candidate Refinement.

$K$	Method	Attribution				Verification				NLI and CoLA
		Reddit		Blog		Reddit		Blog		Reddit		Blog
		MRR $\downarrow$	R@8 $\downarrow$	MRR $\downarrow$	R@8 $\downarrow$	AUC $\approx$ 50	c@1 $\downarrow$	AUC $\approx$ 50	c@1 $\downarrow$	NLI $\uparrow$	CoLA $\uparrow$	NLI $\uparrow$	CoLA $\uparrow$
1	AAST Φ	2.4	3.6	1.7	2.0	50.6	51.7	48.3	48.6	55.1	94.7	55.0	93.6
	NoBundleSel Φ	9.1	14.5	6.5	8.9	53.3	50.0	52.1	49.0	54.0	95.0	53.6	93.8
2	AAST Φ	5.5	7.3	3.2	5.7	57.4	50.3	53.9	47.4	57.2	94.9	57.4	93.9
	NoBundleSel Φ	14.4	23.4	10.6	16.3	61.1	53.6	62.6	52.5	55.6	95.1	57.6	94.0

Appendix

Table 16: Results for the effect of removing bundle-level selection on Reddit and Blog. NoBundleSel Φ keeps the same settings as AAST Φ , but removes Bundle-level Candidate Scoring and Candidate Refinement.

Dataset	$K$	Evaluation	Metric	$\epsilon=\infty$	$\epsilon=4$	$\epsilon=2$	$\epsilon=1$
Reddit	1	Attribution	MRR( $\downarrow$ )	2.4	3.1	2.7	3.0
			R@8( $\downarrow$ )	3.6	4.0	2.8	3.2
		Verification	AUC( $\approx$ 50)	50.6	56.0	58.5	54.4
			c@1( $\downarrow$ )	51.7	50.8	53.4	48.7
		Utility	NLI( $\uparrow$ )	55.1	53.4	53.7	52.3
			CoLA( $\uparrow$ )	94.7	94.9	94.9	94.8

Appendix

Table 17: Attribution risk, verification risk, and utility of AAST Φ on Reddit and Blog under different privacy budgets. Results are reported for

K=1,8,16

Method	Observed Strengths	Observed Weaknesses	Main Pattern
AAST	Reduces aggregation linkability and preserves main events in qualitative examples. Does not require training generator.	May still reduce exact semantic entailment, especially in cross-genre rewriting. Some private topics and events can remain because utility is preserved.	Best privacy–utility balance under aggregation, with bundle-level selection reducing author signals across multiple released texts.
KiP	Often preserves local meaning and many details of private text.	Shows weaker privacy under aggregation. Qualitative examples suggest that outputs can stay close to private wording and structure, and may contain fragmented words or control artifacts. Requires model adaptation through fine tuning.	Strong meaning preservation, but higher authorship carryover and less stable output form.
JAMDEC	Can generate longer synthetic texts and does not require task specific generator fine tuning.	Longer outputs often contain repetitive or weakly grounded content, with substantial semantic drift in qualitative examples. It also has much higher generation time due to over generation and filtering.	Distance from the private text can help privacy, but often at the cost of meaning preservation and computational efficiency.

Appendix

Table 18: Qualitative summary of observed strengths and weaknesses. The table summarizes patterns from the quantitative results and qualitative examples.

Method	Bundle	Text
Private	Query ID:1	My mom just got diagnosed with Parkinson’s this week and we are pretty devastated . She just turned 60 , and I didn’t expect to have to confront major health problems so soon. We don’t know much about this disease and are scared and devastated , to say the least. She asked me to help her find resources so that she can understand her disease better , as she’s not internet-savvy . Can anyone please help me with this? Where can we read more about this disease? Are there any types of support that might be available to her? Anything I need to know about how to support her ?
	Query ID:2	Have any of you gone through a grieving process after leaving the church ? I have been a jack Mormon for many years, and just in the past couple of years have I finally fully lost my faith and left completely. I was already living the "worldly" life, so there hasn’t been much to gain since I left, but there has been a lot to lose. I am terrified of death now, I feel much more pessimistic about the future and cynical about life, and sometimes I miss that feeling of being part of a community. To make matters worse, my mom was just diagnosed with a degenerative disease that will probably kill her . I find myself actually wishing that I had some belief system and community support to sustain me through the shock and grief I feel from her diagnosis, but I don’t know that I can ever believe in any religion again. I am grieving for my mom , and grieving for all I feel that I lost when I lost my faith. I feel like I will be crushed under the weight of all of this grief.
	Target ID:1	I’m a student taking evening classes and have also been working full time to pay the bills. My job is getting steadily more stressful and demanding , and I’m struggling to mentally cope and to perform my duties. But I took some tuition assistance from my employer to pay for school, and their stipulation is that I have to stay at this job until at least Jan of 2021 or I have to pay back all the money (which I can’t do). I am losing my mind because of how much I hate this job , and I need some advice on how to keep my sanity . What keeps you sane when you’re stuck at a job you hate but can’t afford to leave ?
	Target ID:2	I just need to vent . I have a friend who has to come home early from her mission due to mental health issues . I just had dinner with her the other day and was saddened to see how much she’s struggling with feelings of failure from being home, compounded by the incredibly insensitive comments some members have made. It was so hard for me to keep my mouth shut while I was with her, because I was just boiling inside with rage at the church for making young folks feel this way. I had to go home early from my mission also, and although I returned eventually I never fully got over the guilt until I left the church. Missions are emotionally (and sometimes physically) traumatic for many people, and the church does NOT take care of those kids the way it should protect its UNPAID VOLUNTEERS . I could go on and on about everything that is fucked up about the way missions are run .
AAST M	Query ID:1	The news that my mother was recently diagnosed with Parkinson’s has left our family deeply shaken . She was looking forward to enjoying her retirement , and this unexpected diagnosis has been incredibly difficult to process. The reality of facing such a serious health challenge so soon is overwhelming. We’re still learning about the disease and feel a mix of fear and uncertainty about what lies ahead. My mom, who isn’t comfortable navigating the internet , has asked for my help in finding reliable information so she can better understand her condition. If anyone has recommendations for trustworthy resources , I’d be so grateful for your guidance. Where should we turn for accurate and helpful information ?
	Query ID:2	The weight of my grief is twofold: I mourn the absence of my mother , and I mourn the dissolution of the beliefs that once shaped my world. For years, I identified as a jack Mormon , but only recently did I fully sever my ties to the faith . The transition hasn’t been a revelation or a release; it’s been a quiet, aching loss . I was already living outside the church expectations, so the departure didn’t bring new freedoms, only a deepening sense of emptiness . The absence of faith hasn’t lifted me; it’s left me adrift .

Appendix

Table 19: Qualitative examples on Reddit at

K

=2. This part shows the private bundle and the AAST outputs. Bold text marks key semantic elements in the private texts and corresponding preserved content in the synthetic texts.

Method	Bundle	Text
KiP	Query ID:1	My mother was just diagnosed with p y lor i this week, and we are pretty devastated . She just turned 60 , and I didn’t expect to have to confront major health problems so soon. We don’t know much about this disease and are, to say the least, scared and devastated . She asked me to help her find resources so that she might better understand her disease, as she is not web-savvy . Can anyone please help me with this?. Where can we read more about this disease?. A re there any kinds of support that might be available to her?. Anything I need to know about how to support her ?
	Query ID:2	H a ve any of you gone through a griev ing process after leaving the church ?. I have been a jack-mor mon for many years, and only in the last couple of years have I completely lost my faith and left completely. I was already living a life of the world ly life. So there has n’t been much to gain since I left, but there has been a lot to lose. I am now more f right ened of death ; I feel much more pe s s im istic about the future and c y n ical about life ; and sometimes I miss that feeling of being part of a community. To make matters worse, my mother was just diagnosed with a de generative disease that will probably kill her . And really, I find myself wanting that I had some belief system and community support to sustain me through the shock and grief I feel from her diagnosis, but I don’t know that I can ever again believe in any religion . I am griev ing for my mother , and griev ing for all that I feel that I have lost when I have lost my faith . I feel as if I shall be crushed under the weight of all these grief s
	Target ID:1	I am a student taking evening classes and have been working full time to pay the bills. lexic al = 60. My job is steadi ly more difficult and more demanding ; and I am struggling to cope mentally and perform my duties. But I took some tuition assistance from my employer to pay for school, and their s tipul ation is that I have to stay at this job until at least Jan of 20 21, or I must pay back all the money , which I cannot do. I am losing my mind because of how much I hate this job . I need some advice on how to keep my s an ity . What keeps you s an e when you are stuck at a job you hate but can’t afford to leave ?
	Target ID:2	I just need to vent . lexic al = 60. And order = 60. I have a friend who has to come home early from her mission because of mental health problems . I just had dinner with her the other day and was sad d ened to see how much she was struggling with feelings of failure from being at home, compound e d by the incredibly in sensitive comments some members made. I t was so hard for me to keep my mouth shut while I was with her, because I was boiling inside with rage at the church for making young people feel this way. …(omitted)
JAMDEC	Query ID:1	We are trying to figure out what we are going to do with the money, but we don’t know if we will be able to take care of her for the rest of our lives or if she will need to be in a nursing home for the remainder of her life, so we are just trying to get as much money as we can to help her out as much as possible while we are still able to do so! Thank you so much for taking the time to read our story and if you are able to help us out , we would love to hear from you so we can thank you for your kindness! ! …(omitted)
	Query ID:2	If so, what was it like for you, and what advice would you give to someone who is going through the same thing right now? Please share your thoughts and experiences in the comments section below, and if you’d like to learn more about this topic, check out my book, Becoming a Supporter (affiliate link). If so, what was it like for you, and what advice would you give to someone who is going through the same thing right now? I was raised in the church , went to seminary, and served a mission, but after a few years I realized that the church was full of lies and that I had been lied to for so long, that I just couldn’t take it anymore. …(omitted)

Appendix

Table 20: Qualitative examples on Reddit at

K

=2, continued. This part shows KiP and JAMDEC outputs for the same private bundle in Table 19 . Bold text marks retained content related to the private text.

Subreddits
frugal	povertyfinance	poverty
poor	assistance	homeless
almosthomeless	tenanthelp	section8publichousing
unemployment	foodstamps	povertykitchen
debt	bankruptcy	borrow
studentloans	medicaid

Appendix

Table 21: Subreddits and filtering keywords used to construct the Reddit dataset.

Text Generation

Text generation studies methods for producing coherent, useful language from prompts, data, and dialogue context.

See all 423 papers

I Am No One: Style-Aware Paraphrasing for Text Anonymization
Sep 11, 2026Ahmed Sohair Khan, Estrid He, Monica Wachowicz +1Authorship Attribution Text Generation
RMIT University, Melbourne, Australia
Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks
Jun 30, 2026Guangsheng Bao, Lihua Rong, Yanbin Zhao +3AI-Generated Text Detection Robustness of Machine-Generated Text Detection
Zhejiang University · Zhejiang University of Technology · Shanghai Polytechnic University +2
AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection
Apr 13, 2026Aria Nourbakhsh, Adelaide Danilov, Christoph Schommer +1AI-Generated Text Detection Robustness of Machine-Generated Text Detection
Department of Computer Science, University of Luxembourg

Sep 11, 2026J/K move · Enter open · S save

I Am No One: Style-Aware Paraphrasing for Text Anonymization

Ahmed Sohair Khan, Estrid He, Monica Wachowicz, Elham Naghizade

RMIT University, Melbourne, Australia

Authorship Attribution Text Generation

Open paper AskPDF arXiv

Authorship attribution models can re-identify users from seemingly anonymized text by exploiting stable stylistic fingerprints, even after explicit identifiers are removed, posing a growing privacy risk for text publishing and analytics. This risk extends to speech-derived text such as ASR transcripts of meetings and call-center conversations, where stylometric leakage can persist even after acoustic anonymization. Differential privacy-based anonymization often severely degrades text quality and utility. We propose a style-aware, prompt-driven anonymization approach that uses pretrained large language models to construct compact stylistic profiles from minimal samples and rewrite text to suppress identifiable style markers while preserving meaning. Across blog and review datasets, our approach reduces authorship attribution F1 by 60-70% while maintaining content quality and readability, substantially outperforming DP-based and non-DP baselines.

Abstract

Text Generation

Explore similar work

I Am No One: Style-Aware Paraphrasing for Text Anonymization

Triospect: A Three-Dimensional Framework for Robust Statistical AI-Generated Text Detection Against Diverse Attacks

AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection