Papers with Code 아카이브
Image Steganography
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
Image Steganography is the main content of information hiding. The sender conceal a secret message into a cover image, then get the container image called stego, and finish the secret message’s transmission on the public channel by transferring the stego image. Then the receiver part of the transmission can reveal the secret message out. Steganalysis is an attack to the steganography algorithm. The listener on the public channel intercept the image and analyze whether the image contains secret information.
Source: Invisible Steganography via Generative Adversarial Networks
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/JapsimarSinghWahi/DeepSteganography
공식 코드https://github.com/Vill-Lab/2023-TIFS-DTIBA
공식 코드Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification
https://github.com/adamcavendish/BASN-Learning-Steganography-with-Binary-Attention-Mechanism
공식 코드BASN -- Learning Steganography with Binary Attention Mechanism
https://github.com/albblgb/cs-fnns
공식 코드Cover-separable Fixed Neural Network Steganography via Deep Generative Models
https://github.com/boschresearch/Stegaformer
공식 코드https://github.com/chaos-boops/clpstnet
공식 코드https://github.com/chaos-boops/stcl
공식 코드STCL:Curriculum learning Strategies for deep learning image steganography models
https://github.com/cxy1997/LISO
공식 코드Learning Iterative Neural Optimizers for Image Steganography
https://github.com/evtricks/diffstega
공식 코드DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models
https://github.com/haifeng-jiang/stegainr4mih
공식 코드StegaINR4MIH: steganography by implicit neural representation for multi-image hiding
https://github.com/karlchahine/neural-cover-selection-for-image-steganography
공식 코드https://github.com/nickd16/steganographic-augmentations
공식 코드https://github.com/starscream-11813/screedsolo-steganography
공식 코드https://github.com/van-zx/natias
공식 코드Natias: Neuron Attribution based Transferable Image Adversarial Steganography
https://github.com/vvictoryuki/cross
공식 코드CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image Steganography
https://github.com/yanghangai/dkis
공식 코드DKiS: Decay weight invertible image steganography with private key
https://github.com/yanghangai/pris
공식 코드PRIS: Practical robust invertible network for image steganography
https://github.com/Heaven-hand/GAN
https://github.com/IricsDo/Data-Hidding-Using-GAN
https://github.com/Neykah/isgan
https://github.com/SejPal/SteganoGAN
https://github.com/Uranium-Deng/Steganalysis-StegoRemoval
Destruction of Image Steganography using Generative Adversarial Networks
https://github.com/junhyeog/EDS
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/m607stars/multiimagesteganography
https://github.com/marcovaldong/isgan
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/qzramiz/End-To-End-Image-Steganography
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/saadzia10/Steganography-Deep-Learning
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/tiennhamhd/steganoganhd
https://github.com/zhipeixu/fakeshield
EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.