An End-to-End Approach for Seam Carving Detection Using Deep Neural Networks

Autor(es)

Moreira, Thierry P. ; Santana, Marcos Cleison S. ; Passos, Leandro A. ; Papa, Joao Paulo ; da Costa, Kelton Augusto P. ; Pinho, AJ ; Georgieva, P ; Teixeira, LF ; Sanchez, JA

• Tipo de documento: Artigo Científico
• Fonte: PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2022); v. 13256, p. 11-pg., 2022-01-01.

Resumo

Seam carving is a computational method capable of resizing images for both reduction and expansion based on its content, instead of the image geometry. Although the technique is mostly employed to deal with redundant information, i.e., regions composed of pixels with similar intensity, it can also be used for tampering images by inserting or removing relevant objects. Therefore, detecting such a process is of extreme importance regarding the image security domain. However, recognizing seam-carved images does not represent a straightforward task even for human eyes, and robust computation tools capable of identifying such alterations are very desirable. In this paper, we propose an end-to-end approach to cope with the problem of automatic seam carving detection that can obtain state-of-the-art results. Experiments conducted over public and private datasets with several tampering configurations evidence the suitability of the proposed model. (AU)

• Processo FAPESP: 21/05516-1 - Sobre a aplicação das técnicas da Explainable Artificial Intelligence (XAI) para geração de imagens a partir de pacotes de dados para detecção de anomalias em redes de computadores
• Beneficiário: Kelton Augusto Pontara da Costa
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 19/07665-4 - Centro de Inteligência Artificial
• Beneficiário: Fabio Gagliardi Cozman
• Modalidade de apoio: Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa em Engenharia
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs