A Private Watermark for Large Language Models

Recently, text watermarking algorithms for large language models (LLMs) have been mitigating the potential harms of text generated by the LLMs, including fake news and copyright issues. However, the watermark detection of current text algorithms requires the key from the generation process, making them susceptible to breaches and counterfeiting. In this work, we propose the first private watermarking algorithm, which extends the current text watermarking algorithms by using two different neural networks respectively for watermark generation and detection, rather than using the same key at both stages. Meanwhile, part of the parameters of the watermark generation and detection networks are shared, which makes the detection network achieve a high accuracy very efficiently. Experiments show that our algorithm ensures high detection accuracy with minimal impact on generation and detection speed, due to the small parameter size of both networks. Additionally, our subsequent analysis demonstrates the difficulty of reverting the watermark generation rules from the detection network.Comment: 13 pages, 3 figures, 3 table

Copyright protection for the electronic distribution of text documents

Each copy of a text document can be made different in a nearly invisible way by repositioning or modifying the appearance of different elements of text, i.e., lines, words, or characters. A unique copy can be registered with its recipient, so that subsequent unauthorized copies that are retrieved can be traced back to the original owner. In this paper we describe and compare several mechanisms for marking documents and several other mechanisms for decoding the marks after documents have been subjected to common types of distortion. The marks are intended to protect documents of limited value that are owned by individuals who would rather possess a legal than an illegal copy if they can be distinguished. We will describe attacks that remove the marks and countermeasures to those attacks. An architecture is described for distributing a large number of copies without burdening the publisher with creating and transmitting the unique documents. The architecture also allows the publisher to determine the identity of a recipient who has illegally redistributed the document, without compromising the privacy of individuals who are not operating illegally. Two experimental systems are described. One was used to distribute an issue of the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, and the second was used to mark copies of company private memoranda

Publicly Detectable Watermarking for Language Models

We construct the first provable watermarking scheme for language models with public detectability or verifiability: we use a private key for watermarking and a public key for watermark detection. Our protocol is the first watermarking scheme that does not embed a statistical signal in generated text. Rather, we directly embed a publicly-verifiable cryptographic signature using a form of rejection sampling. We show that our construction meets strong formal security guarantees and preserves many desirable properties found in schemes in the private-key watermarking setting. In particular, our watermarking scheme retains distortion-freeness and model agnosticity. We implement our scheme and make empirical measurements over open models in the 7B parameter range. Our experiments suggest that our watermarking scheme meets our formal claims while preserving text quality

Print-Scan Resilient Text Image Watermarking Based on Stroke Direction Modulation for Chinese Document Authentication

Print-scan resilient watermarking has emerged as an attractive way for document security. This paper proposes an stroke direction modulation technique for watermarking in Chinese text images. The watermark produced by the idea offers robustness to print-photocopy-scan, yet provides relatively high embedding capacity without losing the transparency. During the embedding phase, the angle of rotatable strokes are quantized to embed the bits. This requires several stages of preprocessing, including stroke generation, junction searching, rotatable stroke decision and character partition. Moreover, shuffling is applied to equalize the uneven embedding capacity. For the data detection, denoising and deskewing mechanisms are used to compensate for the distortions induced by hardcopy. Experimental results show that our technique attains high detection accuracy against distortions resulting from print-scan operations, good quality photocopies and benign attacks in accord with the future goal of soft authentication

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

The rise in malicious usage of large language models, such as fake content creation and academic plagiarism, has motivated the development of approaches that identify AI-generated text, including those based on watermarking or outlier detection. However, the robustness of these detection algorithms to paraphrases of AI-generated text remains unclear. To stress test these detectors, we build a 11B parameter paraphrase generation model (DIPPER) that can paraphrase paragraphs, condition on surrounding context, and control lexical diversity and content reordering. Using DIPPER to paraphrase text generated by three large language models (including GPT3.5-davinci-003) successfully evades several detectors, including watermarking, GPTZero, DetectGPT, and OpenAI's text classifier. For example, DIPPER drops detection accuracy of DetectGPT from 70.3% to 4.6% (at a constant false positive rate of 1%), without appreciably modifying the input semantics. To increase the robustness of AI-generated text detection to paraphrase attacks, we introduce a simple defense that relies on retrieving semantically-similar generations and must be maintained by a language model API provider. Given a candidate text, our algorithm searches a database of sequences previously generated by the API, looking for sequences that match the candidate text within a certain threshold. We empirically verify our defense using a database of 15M generations from a fine-tuned T5-XXL model and find that it can detect 80% to 97% of paraphrased generations across different settings while only classifying 1% of human-written sequences as AI-generated. We open-source our models, code and data.Comment: NeurIPS 2023 camera ready (32 pages). Code, models, data available in https://github.com/martiansideofthemoon/ai-detection-paraphrase

Fusion of Steganography Digital Watermarking Data Hidden In Patient Medical Image using PPC Approach

Privacy is a critical issue when the patient message storage or processing to the medical services. Digital Image processing is the quick emerging area of medical science. The improvement of image processing was given by the technology improvement like digital visualizing, computer processor and large storage devices. Image processing allowed to compute the image in multidimensional within the system. First, the real problem becomes many severe due to the decrease of visual proofs in telehealth applications. A watermark is a protect message that message hidden into a mask message. Digital image watermarks are used for check the approval of the carrier signal for confirmation of the owners. In order to give information honesty, confidentiality and authentication various approaches are accessible like networking side cryptography, image processing side steganography and digital watermarking. To protect the patient message in telehealth, hidden into a mask message is recently used. Patient details are watermark within the cover medical image. The public and personal key cryptography (PPC) is insufficient for providing the trust a patient may attain during a face-to-face service

Digital watermarking and novel security devices

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Towards Possibilities & Impossibilities of AI-generated Text Detection: A Survey

Large Language Models (LLMs) have revolutionized the domain of natural language processing (NLP) with remarkable capabilities of generating human-like text responses. However, despite these advancements, several works in the existing literature have raised serious concerns about the potential misuse of LLMs such as spreading misinformation, generating fake news, plagiarism in academia, and contaminating the web. To address these concerns, a consensus among the research community is to develop algorithmic solutions to detect AI-generated text. The basic idea is that whenever we can tell if the given text is either written by a human or an AI, we can utilize this information to address the above-mentioned concerns. To that end, a plethora of detection frameworks have been proposed, highlighting the possibilities of AI-generated text detection. But in parallel to the development of detection frameworks, researchers have also concentrated on designing strategies to elude detection, i.e., focusing on the impossibilities of AI-generated text detection. This is a crucial step in order to make sure the detection frameworks are robust enough and it is not too easy to fool a detector. Despite the huge interest and the flurry of research in this domain, the community currently lacks a comprehensive analysis of recent developments. In this survey, we aim to provide a concise categorization and overview of current work encompassing both the prospects and the limitations of AI-generated text detection. To enrich the collective knowledge, we engage in an exhaustive discussion on critical and challenging open questions related to ongoing research on AI-generated text detection

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims