Learning Discriminative Noise Guidance for Image Forgery Detection and Localization
Authors: Jiaying Zhu, Dong Li, Xueyang Fu, Gang Yang, Jie Huang, Aiping Liu, Zheng-Jun Zha
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on multiple datasets demonstrate that our method can reliably detect and localize forgeries, surpassing existing state-of-the-art methods. |
| Researcher Affiliation | Academia | University of Science and Technology of China {zhujy53, dongli6, yg1997}@mail.ustc.edu.cn, {xyfu, aipingl, zhazj}@ustc.edu.cn |
| Pseudocode | Yes | Algorithm 1: Cross-attention-based Guided Filter (CAGF) Input: Gn: guidance image(Noise), Gr: input image(RGB), Output: q: CAGF filtering output. 1: meann = MFConv(Gn) meanr = MFConv(Gr) 2: # Calculate variance and covariance (CMA: Figure 3) varn = CMA(Gn, Gn) covnr = CMA(Gn, Gr) 3: # Calculate coefficients of local linear relationship a = Res Block(Concat(covnr, varn)) b = meanr a meann 4: meana = MFConv(a) meanb = MFConv(b) 5: # Output q = meana * Gn + meanb return q |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate our model on CASIA (Dong, Wang, and Tan 2013), Coverage (Wen et al. 2016), Columbia (Hsu and Chang 2006), Nist Nimble 2016 (NIST16) (Guan et al. 2019) and IMD20 (Novozamsky, Mahdian, and Saic 2020). |
| Dataset Splits | No | The paper mentions training and testing splits, but does not explicitly provide details about validation splits or the specific proportions of data used for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions various models and layers used (e.g., CBDNet, ResNet-50), but does not provide specific version numbers for these or any underlying software dependencies like programming languages, frameworks, or libraries. |
| Experiment Setup | Yes | Ln = λ (1 JSD) + (1 λ) L (Y, Gc) , (6) ... where λ is the hyperparameter to balance the two terms which is set as 0.80. ... LN = αL1 (Y, Gout)+βL2 (y, Dout)+(1 α β) L3 E , Ge , (13) ... In practice, α is set as 0.60 and β is set as 0.2. |