7 research outputs found
Hiding Functions within Functions: Steganography by Implicit Neural Representations
Deep steganography utilizes the powerful capabilities of deep neural networks
to embed and extract messages, but its reliance on an additional message
extractor limits its practical use due to the added suspicion it can raise from
steganalyzers. To address this problem, we propose StegaINR, which utilizes
Implicit Neural Representation (INR) to implement steganography. StegaINR
embeds a secret function into a stego function, which serves as both the
message extractor and the stego media for secure transmission on a public
channel. Recipients need only use a shared key to recover the secret function
from the stego function, allowing them to obtain the secret message. Our
approach makes use of continuous functions, enabling it to handle various types
of messages. To our knowledge, this is the first work to introduce INR into
steganography. We performed evaluations on image and climate data to test our
method in different deployment contexts
Image Steganography: A Review of the Recent Advances
Image Steganography is the process of hiding information which can be text, image or video inside a cover image. The secret information is hidden in a way that it not visible to the human eyes. Deep learning technology, which has emerged as a powerful tool in various applications including image steganography, has received increased attention recently. The main goal of this paper is to explore and discuss various deep learning methods available in image steganography field. Deep learning techniques used for image steganography can be broadly divided into three categories - traditional methods, Convolutional Neural Network-based and General Adversarial Network-based methods. Along with the methodology, an elaborate summary on the datasets used, experimental set-ups considered and the evaluation metrics commonly used are described in this paper. A table summarizing all the details are also provided for easy reference. This paper aims to help the fellow researchers by compiling the current trends, challenges and some future direction in this field
Unbiased Watermark for Large Language Models
The recent advancements in large language models (LLMs) have sparked a
growing apprehension regarding the potential misuse. One approach to mitigating
this risk is to incorporate watermarking techniques into LLMs, allowing for the
tracking and attribution of model outputs. This study examines a crucial aspect
of watermarking: how significantly watermarks impact the quality of
model-generated outputs. Previous studies have suggested a trade-off between
watermark strength and output quality. However, our research demonstrates that
it is possible to integrate watermarks without affecting the output probability
distribution with appropriate implementation. We refer to this type of
watermark as an unbiased watermark. This has significant implications for the
use of LLMs, as it becomes impossible for users to discern whether a service
provider has incorporated watermarks or not. Furthermore, the presence of
watermarks does not compromise the performance of the model in downstream
tasks, ensuring that the overall utility of the language model is preserved.
Our findings contribute to the ongoing discussion around responsible AI
development, suggesting that unbiased watermarks can serve as an effective
means of tracking and attributing model outputs without sacrificing output
quality
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Empowering Responsible Use of Large Language Models
The rapid advancement of powerful Large Language Models (LLMs), such as ChatGPT and Llama, has revolutionized the world by bringing new creative possibilities and enhancing productivity. However, these advancements also pose significant challenges and risks, including the potential for misuse in the form of fake news, academic dishonesty, intellectual property infringements, and privacy leaks. In response to these concerns, this thesis explores approaches to promoting the responsible use of LLMs from both theoretical and empirical perspectives.Three key approaches are presented: (1) Detecting AI-generated Text via Watermarking: We propose a robust and high-quality watermarking method called Unigram-Watermark and introduce a rigorous theoretical framework to quantify the effectiveness and robustness of LLM watermarks. Furthermore, we propose PF-Watermark, which achieves the best balance of high detection accuracy and low perplexity. (2) Protecting the Intellectual Property of LLMs: We safeguard the intellectual property of LLMs through novel watermarking techniques designed to prevent model-stealing attacks in both text classification and text generation tasks. (3) Privacy-Preserving LLMs: We employ Confidential Redacted Training (CRT) to train and fine-tune language generation models while protecting sensitive information. In summary, we propose a suite of algorithms and solutions to address LLMs' trending safety, security, and privacy concerns. We hope our studies provide valuable insights for researchers to explore exciting future research solutions that promote responsible AI development and deployment
ICTERI 2020: ІКТ в освіті, дослідженнях та промислових застосуваннях. Інтеграція, гармонізація та передача знань 2020: Матеріали 16-ї Міжнародної конференції. Том II: Семінари. Харків, Україна, 06-10 жовтня 2020 р.
This volume represents the proceedings of the Workshops co-located with the 16th International Conference on ICT in Education, Research, and Industrial Applications, held in Kharkiv, Ukraine, in October 2020. It comprises 101 contributed papers that were carefully peer-reviewed and selected from 233 submissions for the five workshops: RMSEBT, TheRMIT, ITER, 3L-Person, CoSinE, MROL. The volume is structured in six parts, each presenting the contributions for a particular workshop. The topical scope of the volume is aligned with the thematic tracks of ICTERI 2020: (I) Advances in ICT Research; (II) Information Systems: Technology and Applications; (III) Academia/Industry ICT Cooperation; and (IV) ICT in Education.Цей збірник представляє матеріали семінарів, які були проведені в рамках 16-ї Міжнародної конференції з ІКТ в освіті, наукових дослідженнях та промислових застосуваннях, що відбулася в Харкові, Україна, у жовтні 2020 року. Він містить 101 доповідь, які були ретельно рецензовані та відібрані з 233 заявок на участь у п'яти воркшопах: RMSEBT, TheRMIT, ITER, 3L-Person, CoSinE, MROL. Збірник складається з шести частин, кожна з яких представляє матеріали для певного семінару. Тематична спрямованість збірника узгоджена з тематичними напрямками ICTERI 2020: (I) Досягнення в галузі досліджень ІКТ; (II) Інформаційні системи: Технології і застосування; (ІІІ) Співпраця в галузі ІКТ між академічними і промисловими колами; і (IV) ІКТ в освіті