diff --git a/fdd_defense/defenders/__init__.py b/fdd_defense/defenders/__init__.py
index ea44428..42ba14d 100644
--- a/fdd_defense/defenders/__init__.py
+++ b/fdd_defense/defenders/__init__.py
@@ -5,6 +5,7 @@
 from .regularization import RegularizationDefender
 from .autoencoder import AutoEncoderDefender
 from .atonquant import ATQDefender
+from .defense_gan import DefenseGanDefender, GRUDefenseGanDefender
 
 __all__ = [
     'NoDefenceDefender',
@@ -13,5 +14,7 @@
     'AdversarialTrainingDefender',
     'RegularizationDefender',
     'AutoEncoderDefender',
-    'ATQDefender'
-]
\ No newline at end of file
+    'ATQDefender',
+    'DefenseGanDefender',
+    'GRUDefenseGanDefender',
+]
diff --git a/fdd_defense/defenders/defense_gan.py b/fdd_defense/defenders/defense_gan.py
new file mode 100644
index 0000000..f856f5a
--- /dev/null
+++ b/fdd_defense/defenders/defense_gan.py
@@ -0,0 +1,351 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torch.optim import Adam
+from tqdm.auto import tqdm, trange
+
+from fdd_defense.defenders.base import BaseDefender
+
+
+class Generator(nn.Module):
+    def __init__(
+            self,
+            num_sensors: int,
+            window_size: int,
+            noise_size: int = 256):
+        self.num_sensors = num_sensors
+        self.window_size = window_size
+        self.noise_size = noise_size
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Linear(100, 256),
+            nn.LeakyReLU(True),
+            nn.Linear(256, 256),
+            nn.LeakyReLU(True),
+            nn.Linear(256, 256),
+            nn.LeakyReLU(True),
+            nn.Linear(256, self.num_sensors * self.window_size),
+        )
+
+    def forward(self, x):
+        return self.model(x).view(-1, self.window_size, self.num_sensors)
+
+
+class Discriminator(nn.Module):
+    def __init__(
+            self,
+            num_sensors: int,
+            window_size: int):
+        super().__init__()
+        self.num_sensors = num_sensors
+        self.window_size = window_size
+        self.model = nn.Sequential(
+            nn.Flatten(),
+            nn.Linear(self.num_sensors * self.window_size, 256),
+            nn.ReLU(True),
+            nn.Linear(256, 256),
+            nn.ReLU(True),
+            nn.Linear(256, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class DefenseGanDefender(BaseDefender):
+    def __init__(self, model, random_restarts=10, optim_steps=1000,
+                 optim_lr=0.01, save_loss_history=False):
+        super().__init__(model)
+        self.random_restarts = random_restarts
+        self.optim_steps = optim_steps
+        self.optim_lr = optim_lr
+
+        self.noise_len = 100
+
+        self.device = self.model.device
+
+        self.train_gan(save_loss_history)
+
+        # from self.train_gan:
+        # self.generator (eval mode), [self.gen_loss, self.discr_loss]
+
+    def train_gan(self, save_loss_history=False):
+        window_size = self.model.window_size  # expected 10
+        num_sensors = self.model.dataset.df.shape[1]
+
+        G = Generator(num_sensors=num_sensors, window_size=window_size).to(self.device)
+        D = Discriminator(num_sensors=num_sensors, window_size=window_size).to(self.device)
+
+        num_epochs = 300
+        learning_rate = 1e-4
+
+        G_losses = []
+        D_losses = []
+        iters = 0
+
+        latent_size = 100  # (10, 10)
+        batch_size = 512
+
+        desc_steps = 1
+
+        optim_G = torch.optim.AdamW(G.parameters(), lr=learning_rate)
+        optim_D = torch.optim.RMSprop(D.parameters(), lr=learning_rate)
+
+        criterion = nn.BCELoss()
+
+        for epoch in trange(num_epochs, desc='Epochs ...'):
+            for (data, _, _) in tqdm(self.model.dataloader, desc='Steps ...',
+                                     leave=False):
+
+                # 1. Обучим D: max log(D(x)) + log(1 - D(G(z)))
+                D.train()
+
+                for _ in range(desc_steps):
+                    D.zero_grad()
+                    data = torch.Tensor(data).to(self.device)
+                    batch_size = len(data)
+                    pred = D(data).view(-1)
+                    true = torch.ones(batch_size).to(self.device)
+                    loss_data = criterion(pred, true)
+                    loss_data.backward()
+
+                    z = torch.randn(batch_size, latent_size).to(self.device)
+                    out = G(z)
+                    pred = D(out.detach()).view(-1)
+                    true = torch.zeros(batch_size).to(self.device)
+                    loss_z = criterion(pred, true)
+                    loss_z.backward()
+
+                    D_loss = loss_z + loss_data
+
+                    optim_D.step()
+
+                # 2. Обучим G: max log(D(G(z)))
+                G.train()
+                G.zero_grad()
+
+                D.eval()
+
+                true = torch.ones(batch_size).to(self.device)
+                pred = D(out).view(-1)
+                loss = criterion(pred, true)
+                loss.backward()
+
+                G_loss = loss
+
+                optim_G.step()
+
+                D_losses.append(D_loss.item())
+                G_losses.append(G_loss.item())
+
+                iters += 1
+
+        self.generator = G
+        if save_loss_history:
+            self.gen_loss = G_losses
+            self.discr_loss = D_losses
+        self.generator.eval()
+        self.generator.requires_grad_(False)
+
+    def generate_similar(self, x: torch.Tensor) -> np.ndarray:
+        # (H, W) -> (H, W)
+
+        noise = torch.randn(size=(self.random_restarts, self.noise_len),
+                            device=self.device, requires_grad=True)
+        optimizer = Adam([noise], lr=1e-3)
+
+        for optim_step in range(self.optim_steps):
+            generated_data = self.generator(noise)
+            dist = (generated_data - x).square().mean(dim=(1, 2))
+            loss = dist.sum()
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        noise.requires_grad_(False)
+        generated_data = self.generator(noise)
+        dist = (generated_data - x).square().mean(dim=(1, 2))
+        best_approx = generated_data[dist.argmin()]
+        return best_approx.cpu().numpy()
+
+    def predict(self, batch: np.ndarray) -> np.ndarray:
+        # (N, H, W) -> (N,)
+        batch = torch.FloatTensor(batch).to(self.device)
+        approximations = []
+        for x in tqdm(batch, desc='Iterating over batch ...', leave=False):
+            approximations.append(self.generate_similar(x))
+        return self.model.predict(np.stack(approximations))
+
+
+# GRU version
+
+class SelectItem(nn.Module):
+    def __init__(self, item_index):
+        super(SelectItem, self).__init__()
+        self._name = 'selectitem'
+        self.item_index = item_index
+
+    def forward(self, inputs):
+        return inputs[self.item_index]
+
+
+class GRUGenerator(nn.Module):
+    def __init__(
+            self,
+            num_sensors: int,
+            window_size: int,
+            noise_size: int = 256,
+            ):
+        self.num_sensors = num_sensors
+        self.window_size = window_size
+        self.noise_size = noise_size
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.GRU(self.noise_size, 128, num_layers=1, batch_first=True),
+            SelectItem(0),
+            nn.Linear(128, self.num_sensors),
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class GRUDiscriminator(nn.Module):
+    def __init__(
+            self,
+            num_sensors: int,
+            window_size: int,
+            ):
+        super().__init__()
+        self.num_sensors = num_sensors
+        self.window_size = window_size
+        self.model = nn.Sequential(
+            nn.GRU(num_sensors, 128, num_layers=1, batch_first=True),
+            SelectItem(0),
+            nn.Linear(128, 128),
+            nn.Flatten(),
+            nn.Linear(128 * self.window_size, 1),
+            nn.Sigmoid(),
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+
+class GRUDefenseGanDefender(BaseDefender):
+    def __init__(self, model, random_restarts=1, optim_steps=1000,
+                 optim_lr=1e-3, save_loss_history=False, num_epochs=100):
+        super().__init__(model)
+        self.random_restarts = random_restarts
+        self.optim_steps = optim_steps
+        self.optim_lr = optim_lr
+
+        self.noise_len = 256
+
+        self.device = self.model.device
+
+        self.num_epochs = num_epochs
+
+        self.train_gan(save_loss_history)
+
+        # from self.train_gan:
+        # self.generator (eval mode), [self.gen_loss, self.discr_loss]
+
+    def train_gan(self, save_loss_history=False):
+        window_size = self.model.window_size  # expected 10
+        num_sensors = self.model.dataset.df.shape[1]
+
+        G = GRUGenerator(num_sensors=num_sensors, window_size=window_size).to(self.device)
+        D = GRUDiscriminator(num_sensors=num_sensors, window_size=window_size).to(self.device)
+
+        learning_rate = 1e-4
+
+        G_losses = []
+        D_losses = []
+        iters = 0
+
+        batch_size = 512
+
+        desc_steps = 3
+
+        optim_G = torch.optim.RMSprop(G.parameters(), lr=9 * learning_rate)
+        optim_D = torch.optim.RMSprop(D.parameters(), lr=learning_rate)
+
+        for epoch in trange(self.num_epochs, desc='Epochs ...'):
+            for (data, _, _) in tqdm(self.model.dataloader, desc='Steps ...',
+                                     leave=False):
+
+                # 1. Обучим D: max log(D(x)) + log(1 - D(G(z)))
+                data = torch.Tensor(data).to(self.device)
+                batch_size = len(data)
+
+                for _ in range(desc_steps):
+
+                    with torch.no_grad():
+                        z = torch.randn((batch_size, window_size, self.noise_len), device=self.device)
+                        fake_data = G(z)
+
+                    pred_fake = D(fake_data).squeeze()
+                    loss_fake = F.binary_cross_entropy(pred_fake, torch.zeros(batch_size, device=self.device))
+
+                    pred_true = D(data).squeeze()
+                    loss_true = F.binary_cross_entropy(pred_true, torch.ones(batch_size, device=self.device))
+
+                    loss = loss_fake + loss_true
+
+                    optim_D.zero_grad()
+                    loss.backward()
+
+                    optim_G.zero_grad()
+                    optim_D.step()
+
+                z = torch.randn((batch_size, window_size, self.noise_len), device=self.device)
+                fake_data = G(z)
+                pred = D(fake_data).squeeze()
+                loss = F.binary_cross_entropy(pred, torch.zeros(batch_size, device=self.device))
+
+                # 2. Обучим G: max log(D(G(z)))
+
+                optim_G.zero_grad()
+                loss.backward()
+                optim_D.zero_grad()
+                optim_G.step()
+
+                iters += 1
+
+        self.generator = G
+        if save_loss_history:
+            self.gen_loss = G_losses
+            self.discr_loss = D_losses
+        self.generator.requires_grad_(False)
+
+    def generate_similar(self, x: torch.Tensor) -> np.ndarray:
+        # (H, W) -> (H, W)
+
+        noise = torch.randn(size=(self.random_restarts, self.model.window_size, self.noise_len),
+                            device=self.device, requires_grad=True)
+        optimizer = Adam([noise], lr=self.optim_lr)
+
+        for optim_step in range(self.optim_steps):
+            generated_data = self.generator(noise)
+            dist = (generated_data - x).square().mean(dim=(1, 2))
+            loss = dist.sum()
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        noise.requires_grad_(False)
+        generated_data = self.generator(noise)
+        dist = (generated_data - x).square().mean(dim=(1, 2))
+        best_approx = generated_data[dist.argmin()]
+        return best_approx.cpu().numpy()
+
+    def predict(self, batch: np.ndarray) -> np.ndarray:
+        # (N, H, W) -> (N,)
+        batch = torch.FloatTensor(batch).to(self.device)
+        approximations = []
+        for x in tqdm(batch):
+            approximations.append(self.generate_similar(x))
+        return self.model.predict(np.stack(approximations))
diff --git a/fdd_defense/utils.py b/fdd_defense/utils.py
index f905375..c895303 100644
--- a/fdd_defense/utils.py
+++ b/fdd_defense/utils.py
@@ -1,6 +1,8 @@
 import numpy as np
 from fddbenchmark import FDDDataloader
 from tqdm.auto import tqdm
+from torch import nn
+
 
 def weight_reset(model):
     """
@@ -10,6 +12,7 @@ def weight_reset(model):
     if callable(reset_parameters):
         model.reset_parameters()
 
+
 def accuracy(attacker, defender, step_size):
     test_loader = FDDDataloader(
         dataframe=attacker.model.dataset.df,