Search arXivSearch

arXiv · 2508.07289

Reversible Video Steganography Using Quick Response Codes and Modified ElGamal Cryptosystem

Abstract

The rapid transmission of multimedia information has been achieved mainly by recent advancements in the Internet's speed and information technology. In spite of this, advancements in technology have resulted in breaches of privacy and data security. When it comes to protecting private information in today's Internet era, digital steganography is vital. Many academics are interested in digital video because it has a great capability for concealing important data. There have been a vast number of video steganography solutions developed lately to guard against the theft of confidential data. The visual imperceptibility, robustness, and embedding capacity of these approaches are all challenges that must be addressed. In this paper, a novel solution to reversible video steganography based on DWT and QR codes is proposed to address these concerns. In order to increase the security level of the suggested method, an enhanced ElGamal cryptosystem has also been proposed. Prior to the embedding stage, the suggested method uses the modified ElGamal algorithm to encrypt secret QR codes. Concurrently, it applies two-dimensional DWT on the Y-component of each video frame resulting in LL, LH, HL, and HH sub-bands. Then, the encrypted Low (L), Medium (M), Quantile (Q), and High (H) QR codes are embedded into the HL sub-band, HH sub-band, U-component, and V-component of video frames, respectively, using the LSB technique. As a consequence of extensive testing of the approach, it was shown to be very secure and highly invisible, as well as highly resistant to attacks from Salt & Pepper, Gaussian, Poisson, and Speckle noises, which has an average SSIM of more than 0.91. Aside from visual imperceptibility, the suggested method exceeds current methods in terms of PSNR average of 52.143 dB, and embedding capacity 1 bpp.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ramadhan J. Mstafa. 2025-08-10. Reversible Video Steganography Using Quick Response Codes and Modified ElGamal Cryptosystem. https://doi.org/10.32604/cmc.2022.025791

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

CogenPVG: Cognitive-Enhanced Reflective Multi-Agent Framework for Persuasive Video Generation

Persuasive video generation (PVG) is a valuable yet under-explored research topic. Despite the significant advances in multimodal content generation, AI-empowered automated creation of human-made-like videos with substantial persuasiveness remains a formidable challenge. In this paper, we propose CogenPVG, a novel Cognitive-Enhanced reflective multi-agent framework tailored for Persuasive Video Generation task. Given the topic and stance from the user, we decouple the sophisticated generation process into four sequential stages: argument reasoning, storyboard planning, asset creation, and post-editing, imitating the workflow of human video producers. To ensure high persuasiveness, each stage is equipped with a pair of generator and critic agents, following a reflective refinement scheme grounded in a solid psychological theory of persuasion, the Elaboration Likelihood Model (ELM). In the argument reasoning stage, we generate highly logical and credible reasoning thoughts under the guidance of critical thinking theory, enabling cognitive enhancement via the central route of the ELM. For the other three stages, we generate and optimize multimodal assets, assembling them into a persuasive video guided by theories of heuristics, as the peripheral route of the ELM. To the best of our knowledge, CogenPVG is the first work focused on general persuasive topics, without being confined to commercial purposes. Extensive experiments and comprehensive analysis demonstrate that our framework achieves the best persuasion performance, thereby proving the effectiveness of our proposed multi-agent framework for the PVG task.

cs.MM

TV-AudioRemover: Joint Text-Visual Guided Sound Removal with Multi-Task Hard-Mixture Curriculum

Visual object removal can eliminate a target from video frames, yet its acoustic trace persists in the soundtrack, causing obvious audio-visual inconsistency. Existing video inpainting models operate solely on pixels, while audio editing models, especially for the sound removal task, are typically driven by text and therefore rely on limited single-modal control, which is less effective than multimodal guidance that provides stronger semantic grounding and temporal synchronization cues. In this paper, we present Text-Visual Guided Sound Removal (TV-AudioRemover), a target sound removal framework that leverages the visually edited video together with a natural-language instruction to suppress the sound associated with the removed visual object from the original audio mixture. To acquire high-quality training data, we devise a pipeline to construct a million-scale dataset of single-object audio-visual aligned samples, from which we synthesize mixture-target pairs customized for model training. To effectively leverage visual context and follow instruction intent, we augment the model architecture with task tokens, generalizable instruction modeling, and modality-specific global guidance. We further adopt multi-task training to strengthen task-role comprehension, and employ a hard-mixture curriculum that leverages semantically similar acoustic mixtures during fine-tuning to enhance fine-grained source discrimination. To support evaluation, we present AV-Remove-Bench, a comprehensive audio-visual object removal benchmark, along with dedicated objective metrics and an MLLM-based evaluation protocol. Experiments demonstrate that our method achieves state-of-the-art performance on both subjective and objective metrics. Project page: https://yjx-research.github.io/TV-AudioRemover/.

cs.MM

ROAM-ASD: Robust Open-World Active Speaker Detection with Flexible Multimodal Fusion

Active speaker detection (ASD) requires reliable association between visible faces and acoustic speech, yet existing systems often degrade under challenging domains or incomplete observations. We introduce ROAM-ASD, a robust audiovisual framework that jointly models audio, full-face, and fine-grained mouth representations. A unified joint self-attention mechanism processes all input streams together with modality-agnostic query tokens, enabling direct interaction among available modality inputs. Modality dropout further improves robustness when input streams are unavailable. ROAM-ASD achieves state-of-the-art performance across five ASD benchmarks: 98.8% mAP on WASD, 87.9% on UniTalk, 96.5% on AVA, 99.3% on ASW, and 98.2% on Talkies, improving over previous best systems by 5.1, 4.7, 0.9, 1.0, and 2.1 mAP points, respectively. ROAM-ASD also substantially improves zero-shot cross-dataset generalization and remains robust to missing observations.

cs.MM