Shielding Bangla Text Generation from AI Mimicry

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Author: Denis Avetisyan

Researchers have developed a new watermarking technique to protect text generated by large language models in Bangla, addressing vulnerabilities to sophisticated cross-lingual attacks.

A layered watermarking framework has been devised for Bangla Large Language Models, establishing a method to embed identifying information within the model’s parameters-a technique designed to trace the provenance of generated text and combat the spread of misinformation.
A layered watermarking framework has been devised for Bangla Large Language Models, establishing a method to embed identifying information within the model’s parameters-a technique designed to trace the provenance of generated text and combat the spread of misinformation.

This paper details the design and evaluation of BanglaLorica, a layered watermarking algorithm demonstrating improved robustness against attacks leveraging round-trip translation for low-resource languages.

While authorship attribution and misuse detection are increasingly vital for large language model outputs, existing watermarking techniques often falter when applied to low-resource languages. This paper, ‘BanglaLorica: Design and Evaluation of a Robust Watermarking Algorithm for Large Language Models in Bangla Text Generation’, systematically evaluates current methods for Bangla text generation and reveals significant vulnerability to cross-lingual round-trip translation attacks. To address this, we propose and demonstrate the effectiveness of a layered watermarking strategy, achieving a substantial improvement in post-attack detection accuracy. Does this layered approach represent a viable pathway towards robust, training-free watermarking for other underrepresented languages and contexts?

Deconstructing Authenticity: The Illusion of Originality

The rapid advancement and widespread availability of Large Language Models (LLMs) have introduced a significant challenge to information integrity: establishing the origin and genuineness of digital text. As these models become increasingly adept at producing human-quality content, discerning between machine-generated and human-authored work is becoming remarkably difficult. This poses risks across numerous domains, from academic publishing and journalism to legal documentation and online communication. The ability to convincingly fabricate text raises concerns about misinformation, plagiarism, and the erosion of trust in digital sources. Consequently, a critical need exists for robust methods to verify the provenance of text, ensuring accountability and maintaining the reliability of information in an age of increasingly sophisticated artificial intelligence.

Current techniques designed to embed imperceptible watermarks within machine-generated text face significant limitations. While the goal is to create a robust signature confirming authorship, these methods often degrade the fluency and grammatical correctness of the output, raising suspicions and hindering practical application. More critically, even seemingly subtle alterations to the generated text – known as adversarial attacks – can frequently bypass or completely remove the watermark, rendering it ineffective. This vulnerability stems from the delicate balance between embedding information and maintaining linguistic coherence; aggressive watermarking drastically impacts quality, while subtle approaches are easily disrupted by minor textual modifications, creating a persistent challenge for verifying the authenticity of content produced by large language models.

The challenge of establishing text authenticity is significantly amplified when applied to low-resource languages, notably Bangla. Unlike languages with simpler morphological structures, Bangla’s rich system of inflection, suffixation, and compounding creates a vast combinatorial space of word forms. This inherent complexity makes it difficult to embed watermarks – subtle, detectable signals indicating machine generation – without either degrading the fluency of the text or creating vulnerabilities exploitable by adversarial attacks designed to remove the mark. Existing watermarking techniques, often reliant on predictable patterns or synonym substitutions, struggle to function effectively amidst Bangla’s nuanced grammatical rules and the potential for multiple valid word formations, demanding novel approaches tailored to its unique linguistic features.

Single-layer watermarking successfully generates Bangla outputs in response to example prompts.
Single-layer watermarking successfully generates Bangla outputs in response to example prompts.

Subtle Injections: Engineering Trust into the Machine

Embedding-time watermarking represents a proactive approach to text authentication by integrating the watermark signal during the text generation process, as opposed to applying modifications after content creation. This method directly influences the probability distributions used by language models to select tokens, subtly biasing the output towards a pre-defined set of words or phrases that constitute the watermark. By operating during generation, embedding-time watermarking avoids the potential for detectable alterations inherent in post-hoc methods and offers a more robust solution for identifying machine-generated text and attributing its origin.

KGW Soft Biasing and Exponential Sampling (EXP) are token-level watermarking techniques that subtly alter the text generation process. KGW operates by adjusting the logits-the raw, unnormalized scores output by the language model-of tokens, increasing the probability of selecting tokens from a pre-defined ‘green’ list. EXP, conversely, introduces controlled randomness into the sampling process; it scales the logits by a factor determined by a pseudo-random number generator, effectively biasing the selection towards certain tokens without explicitly favoring a specific list. Both methods manipulate the probability distribution used to select the next token, embedding the watermark signal directly into the generated text.

Token-level watermarking techniques, such as KGW Soft Biasing and Exponential Sampling (EXP), directly influence the probability distributions used during text generation to embed a detectable signal. Rather than altering generated text after creation, these methods subtly shift the likelihood of selecting specific tokens, incorporating the watermark during the generation process itself. Performance evaluations indicate high detection accuracy under typical conditions, with KGW achieving over 88% accuracy and EXP exceeding 91% in identifying watermarked text.

Detection accuracy for both KGW and EXP watermarking schemes remains consistently high across varying generation lengths, even after undergoing round-trip translation.
Detection accuracy for both KGW and EXP watermarking schemes remains consistently high across varying generation lengths, even after undergoing round-trip translation.

BanglaLorica: Forging Resilience in a Complex Tongue

BanglaLorica addresses the specific challenges of watermarking Bangla text generated by Large Language Models (LLMs). Existing watermarking techniques often perform sub-optimally when applied to languages with complex morphology and character sets, such as Bangla. This algorithm is designed to account for the unique linguistic features of Bangla, including its conjunct characters and syllabic structure, which can interfere with standard watermarking methods. Furthermore, BanglaLorica is optimized for the output characteristics of LLMs, mitigating issues related to the generation of unnatural or grammatically incorrect text during the watermarking process, thereby improving both the robustness and imperceptibility of the watermark.

BanglaLorica employs a Layered Watermarking strategy, combining modifications to the input embeddings of the Large Language Model with post-generation alterations to the output text. Embedding-time watermarking introduces subtle perturbations to the model’s input vector space, while post-generation techniques refine the output to further embed the watermark signal. This layered approach enhances robustness against various attacks, including paraphrasing and text laundering, as compromising one layer does not necessarily reveal or eliminate the entire watermark. The combination of these techniques provides a more resilient watermarking solution compared to single-stage methods.

Evaluations of BanglaLorica demonstrate a significant improvement in detection accuracy following Cross-Lingual Round-Trip Translation (RTT) attacks, a technique commonly used to remove or obscure watermarks. Standard single-layer watermarking techniques exhibit a detection accuracy of less than 20% after undergoing RTT. In contrast, BanglaLorica’s layered watermarking approach achieves a detection accuracy ranging from 40% to 50% under the same conditions. This represents a relative improvement of 3 to 4 times compared to single-layer methods, indicating a substantially increased robustness against text laundering attempts.

BanglaLorica’s design prioritizes the preservation of text quality during the watermarking process. Evaluations demonstrate that the algorithm minimizes reductions in fluency, ensuring the watermarked text remains readable and natural. Simultaneously, BanglaLorica maintains high semantic similarity to the original, unwatermarked text, indicating that the core meaning and information content are not altered by the inclusion of the watermark. This is achieved through careful selection of modification locations and magnitudes, focusing on areas least likely to impact human perception or automated semantic analysis.

The pipeline demonstrates a watermarking process vulnerable to a round-trip time (RTT) attack, followed by a detection phase designed to identify manipulated data.
The pipeline demonstrates a watermarking process vulnerable to a round-trip time (RTT) attack, followed by a detection phase designed to identify manipulated data.

Beyond Static Signals: Architecting a Future of Trust

The development of BanglaLorica demonstrates that effective text watermarking extends beyond simply translating existing English-centric techniques. This project specifically addressed the unique complexities of the Bangla language, including its morphology, character set, and common stylistic patterns, to embed a robust, yet imperceptible, signal within generated text. By tailoring the watermarking algorithm to these linguistic features, BanglaLorica achieved a significantly higher level of both detectability and resilience against common removal attacks compared to generic approaches. This success underscores a critical principle: future watermarking schemes must prioritize language-specific adaptation to overcome inherent linguistic challenges and ensure reliable authentication of AI-generated content across diverse languages and cultural contexts.

Post-generation watermarking techniques, such as the Waterfall system, represent a vital advancement in authenticating AI-generated text by embedding signals after content creation. Unlike methods integrated during the generation process, these systems analyze existing text and subtly alter stylistic elements – like synonym choices or phrasing – to encode a unique identifier. This approach creates a ‘shadow’ of information detectable by specialized algorithms, providing a crucial secondary layer of security. Should the primary authentication method be compromised or circumvented, this watermark persists, verifying origin and bolstering resilience against malicious manipulation. The redundancy inherent in combining both pre- and post-generation watermarking offers a significantly more robust defense against increasingly sophisticated forgery attempts and is critical for building trust in a world saturated with synthetic content.

The efficacy of current text watermarking techniques hinges on a static approach, embedding signals consistently across all generated content; however, future advancements necessitate a shift towards adaptive schemes. These systems would analyze the nuances of each text – its length, complexity, stylistic choices, and even the specific AI model used for creation – to dynamically tailor the watermark’s embedding. Such adaptation would not only enhance robustness against increasingly sophisticated attacks designed to remove or circumvent static watermarks, but also minimize perceptibility, addressing concerns about noticeable alterations to the text’s natural flow. By learning to anticipate and counter evolving attack vectors, and by optimizing watermark placement based on the text’s inherent characteristics, adaptive watermarking promises a more resilient and trustworthy future for AI-generated content authentication.

The proliferation of increasingly sophisticated AI text generation technologies necessitates robust methods for verifying authenticity and preventing the spread of fabricated narratives. Establishing trust in digital content is paramount, and recent advancements in text authentication – including linguistic watermarking and post-generation security layers – represent crucial steps towards this goal. These techniques aim not simply to detect alterations, but to inherently bind a verifiable signal to the generative process itself, offering a means to confidently identify AI-authored text. Without such safeguards, the potential for malicious actors to leverage AI for disinformation campaigns, fraud, and the erosion of public trust becomes significantly heightened, making these authentication innovations essential for maintaining informational integrity in a rapidly evolving digital landscape.

Semantic similarity analysis reveals that both single-layer and layered watermarking techniques effectively preserve the meaning of the original content.
Semantic similarity analysis reveals that both single-layer and layered watermarking techniques effectively preserve the meaning of the original content.

The research detailed within BanglaLorica exemplifies a systematic probing of established boundaries-a core tenet of rigorous inquiry. The layered watermarking approach, designed to withstand cross-lingual attacks, isn’t simply about reinforcing security; it’s about understanding how security fails. As Alan Turing observed, “There is no harm in dreaming about things that are not yet possible.” This paper doesn’t merely accept the premise of watermark vulnerability; it actively dismantles it through experimentation. The success of the layered approach, in bolstering resilience against round-trip translation attacks, demonstrates that true robustness stems from anticipating and neutralizing potential points of failure – a principle echoed in Turing’s own work on codebreaking and machine intelligence. It’s a reverse-engineering of deception, a dismantling of assumptions about what constitutes secure text generation.

Uncharted Territories

The layered approach to watermarking, as demonstrated, offers a temporary reprieve, a more stubborn echo in the face of linguistic scrambling. Yet, the architecture of resilience is inherently provisional. Cross-lingual attacks, particularly those leveraging round-trip translation, are not merely attempts to remove a signal, but to exploit the very act of transformation. Each translation is a controlled demolition, revealing the underlying structure-or lack thereof-of the watermark. Future investigations should not fixate on strengthening the signal, but on embedding it within the noise, accepting that perfect concealment is an illusion.

The focus on Bangla, a low-resource language, highlights a critical asymmetry. Watermarking techniques, often developed and validated on high-resource languages, are then applied to others. This is not transfer learning; it is imposition. The true challenge lies in designing watermarks that are intrinsically sensitive to the nuances of each language, reflecting its unique grammatical structures and semantic ambiguities.

Ultimately, the pursuit of robust watermarking is a symptom of a larger anxiety: the blurring line between creation and imitation. The algorithms themselves are less important than the questions they provoke. If a text can be reliably attributed, does that diminish its artistic merit? If attribution is circumventable, does meaning itself become fluid, untethered from origin? These are not engineering problems; they are philosophical ones, demanding a reassessment of authorship in an age of synthetic text.

Original article: https://arxiv.org/pdf/2601.04534.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-09 20:35